DETAILED ACTION
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kaehler (US 20170255814 A1), in view of Gousev (US 20180173986 A1).
Re Claim 1, KAEHLER discloses a device comprising:
a camera configured to capture images of an eye (see KAEHLER: e. g., Fig. 1A, and, -- For a given camera resolution, an iris image taken when the iris is expanded (e.g., due to increased levels of blue light) and the pupil is constricted will have higher resolution than an image taken when the iris is constricted (and the pupil expanded), because the expanded iris presents a greater area of the iris to the camera. More iris features can be obtained from such an image and better quality iris codes can be generated from such images.--, in [0022]); and
a controller comprising one or more processors (see KAEHLER: e. g., --under control of a hardware computer processor. The method comprises adjusting a level of blue light, receiving an eye image of an eye exposed to the adjusted level of blue light, detecting a change in a pupillary response by comparison of the received eye image to a reference image, determining that the pupillary response corresponds to a biometric characteristic of a human individual, and allowing access to a biometric application based on the pupillary response determination. The method can be performed by a head mounted display system that includes a processor configured to identify a human individual.--, in [0005]-[0006]) configured to:
initiate a user authentication process (see KAEHLER: e. g., --an unauthorized individual may attempt to gain access to the display system and/or system application by imitating (also known as spoofing) the biometric characteristics of an actual authorized user. For example, an unauthorized individual seeking to achieve illicit access to wearable display system may present to the display system a picture (or a 3D model) of the iris of the authorized user. One system application may be projecting images onto the display for a user as if the images appear at different distances from the user. Other examples may include software that allow a user to engage with the Internet, software applications (“apps”), system settings, system security features, etc. The display system may image the iris picture or model and be fooled into permitting access to the unauthorized individual. As another example of improving the security of the wearable display, start-up routines for a wearable display system may incorporate a scene that stimulates the pupillary response. For example, as part of a startup routine that initiates the wearable display system, the display can project an image of a sunrise, where the sky increasingly becomes more blue. Such a scene can stimulate or trigger the pupillary response of the individual wearing the system, so that the wearable display can measure the wearer's pupillary response (e.g., in response to the bluing of the sky) and use the measured response to identify the wearer--, in [0092]);
cause an image to be captured of a current eye under a first set of conditions (see KAEHLER: e. g., --under control of a hardware computer processor. The method comprises adjusting a level of blue light, receiving an eye image of an eye exposed to the adjusted level of blue light, detecting a change in a pupillary response by comparison of the received eye image to a reference image, determining that the pupillary response corresponds to a biometric characteristic of a human individual, and allowing access to a biometric application based on the pupillary response determination. The method can be performed by a head mounted display system that includes a processor configured to identify a human individual.--, in [0005]-[0006]);
transform the image into a current feature representation for the current eye (see KAEHLER: e. g., Fig. 1A, and, -- For a given camera resolution, an iris image taken when the iris is expanded (e.g., due to increased levels of blue light) and the pupil is constricted will have higher resolution than an image taken when the iris is constricted (and the pupil expanded), because the expanded iris presents a greater area of the iris to the camera. More iris features can be obtained from such an image and better quality iris codes can be generated from such images.--, in [0022]);
access a multi-representation eye model, wherein the multi-representation eye model is based on feature representations of images of an identified eye captured under a plurality of different sets of conditions (see KAEHLER: e. g., --[0075] FIG. 7 schematically illustrates an example pupillary response to light adjustment. In addition to the construction and dilation of the pupil as described above with respect to the example of an eye experiencing blue light adjustment, other physiological characteristics of the eye may be affected by the exposure of an increased level of light exposed to the eye 102…[0076] The pupillary response (e.g., pupil parameter, change in a pupil parameter) can include a variety of physiological characteristics, including but not limited to a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased level of light, a rise curve for the rise time, or a decay curve for the decay time…. eye images obtained from imaging system 500 may be used to form a model of the pupillary response. The model may be based on any parameter derived or measured from an eye image, including but not limited to: the pupil area, the pupil radius, the pupil circumference, the pupil diameter, or the outer iris radius relative to the pupil radius. Additionally, or alternatively, physiological characteristics of the pupillary response may be measured by various instruments coupled to the wearable display system 100 or derived from processing eye images. FIG. 7 schematically illustrates parameters representative of a pupillary response based on an eye image analysis of the processing modules 70, 72.--, in [0075]-[0077], and [0080]; and, --For example, processing modules 70, 72 may create an individual biometric model for an individual utilizing the wearable display system 100. To create this individual biometric model, information obtained from eye images may be used to contribute to that model, including but not limited to: a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased and/or decreased level of light, a rise curve for the rise time, or a decay curve for the decay time, a rise time for a pupillary response curve to the adjusted level of blue light, a decay time for the pupillary response curve to the adjusted level of blue light, a delay time to the adjusted level of blue light, a rise curve portion of the pupillary response curve to the adjusted level of blue light, or a decay curve portion of the pupillary response curve to the adjusted level of blue light. Accordingly, the individual biometric model may include a pupillary response under normal light conditions (e.g., ambient lighting conditions) and pupillary response under an adjusted level of blue light. The individual biometric model may also include reference eye images, such as eye images obtained under normal lighting conditions.--, in [0080], [0083]-[0084], and, --[0089] Once constructed, an individual biometric model may be stored in a biometric database. For example, processing modules 70, 72 may communicate via secure communication channel (e.g., the channel is encrypted) with a server hosting a biometric database. Biometric database may store the individual biometric model as a data record corresponding to that specific individual. In this way, the biometric database may store several biometric models obtained from various wearable display systems 100. Additionally, or alternatively, the individual biometric model may be stored locally (e.g., processing module 70). In such a case, the locally stored individual biometric model may be used for identification of an individual utilizing the wearable display system 100. For example, the wearable display system 100 may only allow access or partial access to an individual that matches the locally stored individual biometric model.--. In [0089]-[0091]);
apply the current feature representation to the multi-representation eye model to determine whether the current eye is a match for the identified eye (see KAEHLER: e. g., --[0089] Once constructed, an individual biometric model may be stored in a biometric database. For example, processing modules 70, 72 may communicate via secure communication channel (e.g., the channel is encrypted) with a server hosting a biometric database. Biometric database may store the individual biometric model as a data record corresponding to that specific individual. In this way, the biometric database may store several biometric models obtained from various wearable display systems 100. Additionally, or alternatively, the individual biometric model may be stored locally (e.g., processing module 70). In such a case, the locally stored individual biometric model may be used for identification of an individual utilizing the wearable display system 100. For example, the wearable display system 100 may only allow access or partial access to an individual that matches the locally stored individual biometric model.--. In [0089]-[0091]); and
provide indication of the current eye is a match for the identified eye (see KAEHLER: e. g., --determine that the pupillary response corresponds to specific individuals or specific types of individuals. Access to biometric applications may be granted or denied based on the type of individual identified. Various types of individuals may be identified by their respective pupillary response. In one implementation, access may be denied to a human eye that matches some biometric characteristics, but not others. For example, a deceased human eye may be distinguished from a living human eye based on the pupillary response.--, in [0093]);
KAEHLER however does not explicitly disclose provide indication of whether the current eye is a match for the identified eye;
Gousev discloses provide indication of whether the current eye is a match for the identified eye (see Gousev: e.g., -- the sensor system can send an event to the mobile device's general-purpose microprocessor indicating that the facial features of the user 130 have been recognized and/or causing the mobile device's general-purpose microprocessor to exit the low-power mode and become fully active.--. In [0061]; and,
-- performance of iris-related operations, particularly with respect to power consumption. FIG. 12 shows an embodiment using two different types of sensors—a visual sensor 1202 and an IR sensor 1206. Example implementations of visual sensor 1202 can include sensor system 210 of FIG. 2A, sensor system of FIG. 2B, or visual sensor system 1310 of FIG. 13. Although visual sensor 1202 and IR sensor 1206 are illustrated as two separate sensors, in some implementations described further below, it is understood that CV computation hardware within visual sensor 1202 can perform CV feature computation based on IR images captured by IR sensor 1206. In such implementations, no opening in the front of the phone will be necessary for visual sensor 1202 as both events for activating iris scanning or other iris processing as well as the iris scanning or processing itself can be based on processing of IR images from the IR sensor 1206. … mobile device 1200 may be transitioned to a high-power state to perform iris-related operations, according to this embodiment. The IR light source 1204 may be turned on, and the IR sensor 1206 may be used to capture images of the surroundings illuminated by the IR light source 1204. Images captured by the IR sensor 1206 may be used for iris-related tasks such as iris detection, iris authentication. --, in [0146]-[0147], and [0216], and, -- [0251] Just as an example, in a “two-pass” iris authentication scheme, a “first pass” of iris authentication may focus on large sectors such as sectors 3, 11, and 14. The first pass of iris authentication may involve comparison against the entire collection of known iris data records. However, the burden of performing such a large number of comparisons is offset by the fact that for each comparison, only a few sectors (i.e., large sectors 3, 11, and 14) are evaluated. Based on only the large sectors, the first pass of iris authentication may result in a certain number (e.g., M) of hits. That is, M iris data records out of the entire collection of known iris data records may be identified as potentially matching the iris of the user… if a match in the sector identifiers is found, then the second step of the authentication operation is performed. In particular, CV features are computed for the selected sectors of the iris to be authenticated. Then, the CV features of the selected sectors are compared to the CV features of the corresponding select sectors of registered iris data records. If the CV features of the select sectors of the user's iris match those of a registered iris data record, then the user is authenticated. Otherwise, the user is not authenticated. While the above describes iris authentication based on one iris, similar techniques may be extended to implement iris authentication based on both irises of the user, as mentioned previously. --. in [0251]-[0258]);
KAEHLER and GOUSEV are combinable as they are in the same field of endeavor: using captured eye images and features in authentication. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify KAEHLER’s device using GOUSEV’s teachings by including provide indication of whether the current eye is a match for the identified eye to KAEHLER’s indication of matching the identified eye in order to determine the results of match those of a registered iris data record (see GOUSEV: e.g., in [0003], [0146]-[0147], [0216], and [0251]-[0258]).
Re Claim 2, KAEHLER as modified by GOUSEV further disclose wherein to apply the current feature representation to the multi-representation eye model, the controller is further configured to compare features of the current feature representation to features of the multi-representation eye model (see KAEHLER: e. g., --under control of a hardware computer processor. The method comprises adjusting a level of blue light, receiving an eye image of an eye exposed to the adjusted level of blue light, detecting a change in a pupillary response by comparison of the received eye image to a reference image, determining that the pupillary response corresponds to a biometric characteristic of a human individual, and allowing access to a biometric application based on the pupillary response determination. The method can be performed by a head mounted display system that includes a processor configured to identify a human individual.--, in [0005]-[0006]; also see GOUSEV: e.g., -- [0251] Just as an example, in a “two-pass” iris authentication scheme, a “first pass” of iris authentication may focus on large sectors such as sectors 3, 11, and 14. The first pass of iris authentication may involve comparison against the entire collection of known iris data records. However, the burden of performing such a large number of comparisons is offset by the fact that for each comparison, only a few sectors (i.e., large sectors 3, 11, and 14) are evaluated. Based on only the large sectors, the first pass of iris authentication may result in a certain number (e.g., M) of hits. That is, M iris data records out of the entire collection of known iris data records may be identified as potentially matching the iris of the user… if a match in the sector identifiers is found, then the second step of the authentication operation is performed. In particular, CV features are computed for the selected sectors of the iris to be authenticated. Then, the CV features of the selected sectors are compared to the CV features of the corresponding select sectors of registered iris data records. If the CV features of the select sectors of the user's iris match those of a registered iris data record, then the user is authenticated. Otherwise, the user is not authenticated. While the above describes iris authentication based on one iris, similar techniques may be extended to implement iris authentication based on both irises of the user, as mentioned previously. --. in [0251]-[0258]).
Re Claim 3 and 13, KAEHLER as modified by GOUSEV further disclose wherein the features of the current feature representation and the features of the multi-representation eye model represent different 3D topography, structures, or textures of the current eye captured under the first set of conditions or of the identified eye under the different sets of conditions (see KAEHLER: e. g., --[0075] FIG. 7 schematically illustrates an example pupillary response to light adjustment. In addition to the construction and dilation of the pupil as described above with respect to the example of an eye experiencing blue light adjustment, other physiological characteristics of the eye may be affected by the exposure of an increased level of light exposed to the eye 102…[0076] The pupillary response (e.g., pupil parameter, change in a pupil parameter) can include a variety of physiological characteristics, including but not limited to a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased level of light, a rise curve for the rise time, or a decay curve for the decay time…. eye images obtained from imaging system 500 may be used to form a model of the pupillary response. The model may be based on any parameter derived or measured from an eye image, including but not limited to: the pupil area, the pupil radius, the pupil circumference, the pupil diameter, or the outer iris radius relative to the pupil radius. Additionally, or alternatively, physiological characteristics of the pupillary response may be measured by various instruments coupled to the wearable display system 100 or derived from processing eye images. FIG. 7 schematically illustrates parameters representative of a pupillary response based on an eye image analysis of the processing modules 70, 72.--, in [0075]-[0077], and [0080]; and, --For example, processing modules 70, 72 may create an individual biometric model for an individual utilizing the wearable display system 100. To create this individual biometric model, information obtained from eye images may be used to contribute to that model, including but not limited to: a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased and/or decreased level of light, a rise curve for the rise time, or a decay curve for the decay time, a rise time for a pupillary response curve to the adjusted level of blue light, a decay time for the pupillary response curve to the adjusted level of blue light, a delay time to the adjusted level of blue light, a rise curve portion of the pupillary response curve to the adjusted level of blue light, or a decay curve portion of the pupillary response curve to the adjusted level of blue light. Accordingly, the individual biometric model may include a pupillary response under normal light conditions (e.g., ambient lighting conditions) and pupillary response under an adjusted level of blue light. The individual biometric model may also include reference eye images, such as eye images obtained under normal lighting conditions.--, in [0080], [0083]-[0084], and, --[0089] Once constructed, an individual biometric model may be stored in a biometric database. For example, processing modules 70, 72 may communicate via secure communication channel (e.g., the channel is encrypted) with a server hosting a biometric database. Biometric database may store the individual biometric model as a data record corresponding to that specific individual. In this way, the biometric database may store several biometric models obtained from various wearable display systems 100. Additionally, or alternatively, the individual biometric model may be stored locally (e.g., processing module 70). In such a case, the locally stored individual biometric model may be used for identification of an individual utilizing the wearable display system 100. For example, the wearable display system 100 may only allow access or partial access to an individual that matches the locally stored individual biometric model.--. In [0089]-[0091]; -- One system application may be projecting images onto the display for a user as if the images appear at different distances from the user. Other examples may include software that allow a user to engage with the Internet, software applications (“apps”), system settings, system security features, etc. The display system may image the iris picture or model and be fooled into permitting access to the unauthorized individual. As another example of improving the security of the wearable display, start-up routines for a wearable display system may incorporate a scene that stimulates the pupillary response. For example, as part of a startup routine that initiates the wearable display system, the display can project an image of a sunrise, where the sky increasingly becomes more blue. Such a scene can stimulate or trigger the pupillary response of the individual wearing the system, so that the wearable display can measure the wearer's pupillary response (e.g., in response to the bluing of the sky) and use the measured response to identify the wearer--, in [0092]).
Re Claim 4, KAEHLER as modified by GOUSEV further disclose wherein the features of the current feature representation comprise dependent features dependent on the first set of conditions and independent features independent of the first set of the conditions, and the features of the multi-representation eye model comprise a plurality of different sets of dependent model features dependent on the plurality of the different sets of conditions and a set of independent model features independent of the different sets of conditions, wherein to compare the features of the current feature representation to the features of the multi- representation model the controller is further configured to compare the dependent current features to the plurality of different sets of dependent model features and to compare the independent current features to the set of independent model features (see KAEHLER: e. g., --[0075] FIG. 7 schematically illustrates an example pupillary response to light adjustment. In addition to the construction and dilation of the pupil as described above with respect to the example of an eye experiencing blue light adjustment, other physiological characteristics of the eye may be affected by the exposure of an increased level of light exposed to the eye 102…[0076] The pupillary response (e.g., pupil parameter, change in a pupil parameter) can include a variety of physiological characteristics, including but not limited to a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased level of light, a rise curve for the rise time, or a decay curve for the decay time…. eye images obtained from imaging system 500 may be used to form a model of the pupillary response. The model may be based on any parameter derived or measured from an eye image, including but not limited to: the pupil area, the pupil radius, the pupil circumference, the pupil diameter, or the outer iris radius relative to the pupil radius. Additionally, or alternatively, physiological characteristics of the pupillary response may be measured by various instruments coupled to the wearable display system 100 or derived from processing eye images. FIG. 7 schematically illustrates parameters representative of a pupillary response based on an eye image analysis of the processing modules 70, 72.--, in [0075]-[0077], and [0080]; and, --For example, processing modules 70, 72 may create an individual biometric model for an individual utilizing the wearable display system 100. To create this individual biometric model, information obtained from eye images may be used to contribute to that model, including but not limited to: a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased and/or decreased level of light, a rise curve for the rise time, or a decay curve for the decay time, a rise time for a pupillary response curve to the adjusted level of blue light, a decay time for the pupillary response curve to the adjusted level of blue light, a delay time to the adjusted level of blue light, a rise curve portion of the pupillary response curve to the adjusted level of blue light, or a decay curve portion of the pupillary response curve to the adjusted level of blue light. Accordingly, the individual biometric model may include a pupillary response under normal light conditions (e.g., ambient lighting conditions) and pupillary response under an adjusted level of blue light. The individual biometric model may also include reference eye images, such as eye images obtained under normal lighting conditions.--, in [0080], [0083]-[0084], and, --[0089] Once constructed, an individual biometric model may be stored in a biometric database. For example, processing modules 70, 72 may communicate via secure communication channel (e.g., the channel is encrypted) with a server hosting a biometric database. Biometric database may store the individual biometric model as a data record corresponding to that specific individual. In this way, the biometric database may store several biometric models obtained from various wearable display systems 100. Additionally, or alternatively, the individual biometric model may be stored locally (e.g., processing module 70). In such a case, the locally stored individual biometric model may be used for identification of an individual utilizing the wearable display system 100. For example, the wearable display system 100 may only allow access or partial access to an individual that matches the locally stored individual biometric model.--. In [0089]-[0091]; -- One system application may be projecting images onto the display for a user as if the images appear at different distances from the user. Other examples may include software that allow a user to engage with the Internet, software applications (“apps”), system settings, system security features, etc. The display system may image the iris picture or model and be fooled into permitting access to the unauthorized individual. As another example of improving the security of the wearable display, start-up routines for a wearable display system may incorporate a scene that stimulates the pupillary response. For example, as part of a startup routine that initiates the wearable display system, the display can project an image of a sunrise, where the sky increasingly becomes more blue. Such a scene can stimulate or trigger the pupillary response of the individual wearing the system, so that the wearable display can measure the wearer's pupillary response (e.g., in response to the bluing of the sky) and use the measured response to identify the wearer--, in [0092]; also see GOUSEV: e.g., -- Iris authentication, which involves the comparison of an image of the iris of a user against those of known users, may require numerous operations that are computationally intensive. Exhaustive comparison of the entire region of a user's iris against known data records can require significant computational resources and processing delay. Recognizing that visible features and indeed the distinctiveness of visible features within a human iris are often non-uniformly distributed, the present disclosure presents various approaches for sectorizing the iris and performing iris authentication using only those sectors selected based on a measure of distinctiveness of the sector. Such approaches can greatly improve the efficiency of the iris authentication operation.
[0239] FIG. 27 illustrates a manner by which a plurality of sectors may be defined for an iris region within a captured image of an eye, according to an embodiment of the disclosure. Image 2700 is an image that includes a left eye 2702 and a right eye 2704 of a user. For purposes of illustration, sectorization of the iris region of the left eye is described. Here, the left eye includes a pupil region 2706, an iris region 2708, and a sclera region 2710. An inner circular boundary 2712 separates the pupil region 2706 and the iris region 2708. An outer circular boundary 2714 separates the iris region 2708 and the sclera region 2710.--, in [0179], and [0238]-[0240]; and, -- [0251] Just as an example, in a “two-pass” iris authentication scheme, a “first pass” of iris authentication may focus on large sectors such as sectors 3, 11, and 14. The first pass of iris authentication may involve comparison against the entire collection of known iris data records. However, the burden of performing such a large number of comparisons is offset by the fact that for each comparison, only a few sectors (i.e., large sectors 3, 11, and 14) are evaluated. Based on only the large sectors, the first pass of iris authentication may result in a certain number (e.g., M) of hits. That is, M iris data records out of the entire collection of known iris data records may be identified as potentially matching the iris of the user… if a match in the sector identifiers is found, then the second step of the authentication operation is performed. In particular, CV features are computed for the selected sectors of the iris to be authenticated. Then, the CV features of the selected sectors are compared to the CV features of the corresponding select sectors of registered iris data records. If the CV features of the select sectors of the user's iris match those of a registered iris data record, then the user is authenticated. Otherwise, the user is not authenticated. While the above describes iris authentication based on one iris, similar techniques may be extended to implement iris authentication based on both irises of the user, as mentioned previously. --. in [0251]-[0258]).
Re Claim 5, KAEHLER as modified by GOUSEV further disclose wherein the multi-representation eye model comprises different feature representations for each of the different sets of conditions (see KAEHLER: e. g., --under control of a hardware computer processor. The method comprises adjusting a level of blue light, receiving an eye image of an eye exposed to the adjusted level of blue light, detecting a change in a pupillary response by comparison of the received eye image to a reference image, determining that the pupillary response corresponds to a biometric characteristic of a human individual, and allowing access to a biometric application based on the pupillary response determination. The method can be performed by a head mounted display system that includes a processor configured to identify a human individual.--, in [0005]-[0006]; and, --[0075] FIG. 7 schematically illustrates an example pupillary response to light adjustment. In addition to the construction and dilation of the pupil as described above with respect to the example of an eye experiencing blue light adjustment, other physiological characteristics of the eye may be affected by the exposure of an increased level of light exposed to the eye 102…[0076] The pupillary response (e.g., pupil parameter, change in a pupil parameter) can include a variety of physiological characteristics, including but not limited to a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased level of light, a rise curve for the rise time, or a decay curve for the decay time…. eye images obtained from imaging system 500 may be used to form a model of the pupillary response. The model may be based on any parameter derived or measured from an eye image, including but not limited to: the pupil area, the pupil radius, the pupil circumference, the pupil diameter, or the outer iris radius relative to the pupil radius. Additionally, or alternatively, physiological characteristics of the pupillary response may be measured by various instruments coupled to the wearable display system 100 or derived from processing eye images. FIG. 7 schematically illustrates parameters representative of a pupillary response based on an eye image analysis of the processing modules 70, 72.--, in [0075]-[0077], and [0080]).
Re Claim 6, KAEHLER as modified by GOUSEV further disclose identify the first set of conditions of the current feature representation; determine corresponding one or more feature representations of the multi- representation eye model based on the first set of conditions; and compare the current feature representation to the one or more corresponding feature representations (see KAEHLER: e. g., --under control of a hardware computer processor. The method comprises adjusting a level of blue light, receiving an eye image of an eye exposed to the adjusted level of blue light, detecting a change in a pupillary response by comparison of the received eye image to a reference image, determining that the pupillary response corresponds to a biometric characteristic of a human individual, and allowing access to a biometric application based on the pupillary response determination. The method can be performed by a head mounted display system that includes a processor configured to identify a human individual.--, in [0005]-[0006]; and, --[0075] FIG. 7 schematically illustrates an example pupillary response to light adjustment. In addition to the construction and dilation of the pupil as described above with respect to the example of an eye experiencing blue light adjustment, other physiological characteristics of the eye may be affected by the exposure of an increased level of light exposed to the eye 102…[0076] The pupillary response (e.g., pupil parameter, change in a pupil parameter) can include a variety of physiological characteristics, including but not limited to a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased level of light, a rise curve for the rise time, or a decay curve for the decay time…. eye images obtained from imaging system 500 may be used to form a model of the pupillary response. The model may be based on any parameter derived or measured from an eye image, including but not limited to: the pupil area, the pupil radius, the pupil circumference, the pupil diameter, or the outer iris radius relative to the pupil radius. Additionally, or alternatively, physiological characteristics of the pupillary response may be measured by various instruments coupled to the wearable display system 100 or derived from processing eye images. FIG. 7 schematically illustrates parameters representative of a pupillary response based on an eye image analysis of the processing modules 70, 72.--, in [0075]-[0077], and [0080]; also see GOUSEV: e.g., -- Iris authentication, which involves the comparison of an image of the iris of a user against those of known users, may require numerous operations that are computationally intensive. Exhaustive comparison of the entire region of a user's iris against known data records can require significant computational resources and processing delay. Recognizing that visible features and indeed the distinctiveness of visible features within a human iris are often non-uniformly distributed, the present disclosure presents various approaches for sectorizing the iris and performing iris authentication using only those sectors selected based on a measure of distinctiveness of the sector. Such approaches can greatly improve the efficiency of the iris authentication operation.
[0239] FIG. 27 illustrates a manner by which a plurality of sectors may be defined for an iris region within a captured image of an eye, according to an embodiment of the disclosure. Image 2700 is an image that includes a left eye 2702 and a right eye 2704 of a user. For purposes of illustration, sectorization of the iris region of the left eye is described. Here, the left eye includes a pupil region 2706, an iris region 2708, and a sclera region 2710. An inner circular boundary 2712 separates the pupil region 2706 and the iris region 2708. An outer circular boundary 2714 separates the iris region 2708 and the sclera region 2710.--, in [0179], and [0238]-[0240]; and, -- [0251] Just as an example, in a “two-pass” iris authentication scheme, a “first pass” of iris authentication may focus on large sectors such as sectors 3, 11, and 14. The first pass of iris authentication may involve comparison against the entire collection of known iris data records. However, the burden of performing such a large number of comparisons is offset by the fact that for each comparison, only a few sectors (i.e., large sectors 3, 11, and 14) are evaluated. Based on only the large sectors, the first pass of iris authentication may result in a certain number (e.g., M) of hits. That is, M iris data records out of the entire collection of known iris data records may be identified as potentially matching the iris of the user… if a match in the sector identifiers is found, then the second step of the authentication operation is performed. In particular, CV features are computed for the selected sectors of the iris to be authenticated. Then, the CV features of the selected sectors are compared to the CV features of the corresponding select sectors of registered iris data records. If the CV features of the select sectors of the user's iris match those of a registered iris data record, then the user is authenticated. Otherwise, the user is not authenticated. While the above describes iris authentication based on one iris, similar techniques may be extended to implement iris authentication based on both irises of the user, as mentioned previously. --. in [0251]-[0258]).
Re Claims 7 and 10, KAEHLER as modified by GOUSEV further disclose wherein the first set of conditions and the plurality of the different set of conditions comprise one or more of: lighting affecting the current eye or the identified eye, pose of the current eye or the identified eye, accommodation distance for the current eye or the identified eye, or indication of force applied to the current eye or the identified eye (see KAEHLER: e. g., --under control of a hardware computer processor. The method comprises adjusting a level of blue light, receiving an eye image of an eye exposed to the adjusted level of blue light, detecting a change in a pupillary response by comparison of the received eye image to a reference image, determining that the pupillary response corresponds to a biometric characteristic of a human individual, and allowing access to a biometric application based on the pupillary response determination. The method can be performed by a head mounted display system that includes a processor configured to identify a human individual.--, in [0005]-[0006]; and, --[0075] FIG. 7 schematically illustrates an example pupillary response to light adjustment. In addition to the construction and dilation of the pupil as described above with respect to the example of an eye experiencing blue light adjustment, other physiological characteristics of the eye may be affected by the exposure of an increased level of light exposed to the eye 102…[0076] The pupillary response (e.g., pupil parameter, change in a pupil parameter) can include a variety of physiological characteristics, including but not limited to a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased level of light, a rise curve for the rise time, or a decay curve for the decay time…. eye images obtained from imaging system 500 may be used to form a model of the pupillary response. The model may be based on any parameter derived or measured from an eye image, including but not limited to: the pupil area, the pupil radius, the pupil circumference, the pupil diameter, or the outer iris radius relative to the pupil radius. Additionally, or alternatively, physiological characteristics of the pupillary response may be measured by various instruments coupled to the wearable display system 100 or derived from processing eye images. FIG. 7 schematically illustrates parameters representative of a pupillary response based on an eye image analysis of the processing modules 70, 72.--, in [0075]-[0077], and [0080]; also see GOUSEV: e.g., -- Iris authentication, which involves the comparison of an image of the iris of a user against those of known users, may require numerous operations that are computationally intensive. Exhaustive comparison of the entire region of a user's iris against known data records can require significant computational resources and processing delay. Recognizing that visible features and indeed the distinctiveness of visible features within a human iris are often non-uniformly distributed, the present disclosure presents various approaches for sectorizing the iris and performing iris authentication using only those sectors selected based on a measure of distinctiveness of the sector. Such approaches can greatly improve the efficiency of the iris authentication operation.
[0239] FIG. 27 illustrates a manner by which a plurality of sectors may be defined for an iris region within a captured image of an eye, according to an embodiment of the disclosure. Image 2700 is an image that includes a left eye 2702 and a right eye 2704 of a user. For purposes of illustration, sectorization of the iris region of the left eye is described. Here, the left eye includes a pupil region 2706, an iris region 2708, and a sclera region 2710. An inner circular boundary 2712 separates the pupil region 2706 and the iris region 2708. An outer circular boundary 2714 separates the iris region 2708 and the sclera region 2710.--, in [0179], [0199], and [0238]-[0240]; and, -- [0251] Just as an example, in a “two-pass” iris authentication scheme, a “first pass” of iris authentication may focus on large sectors such as sectors 3, 11, and 14. The first pass of iris authentication may involve comparison against the entire collection of known iris data records. However, the burden of performing such a large number of comparisons is offset by the fact that for each comparison, only a few sectors (i.e., large sectors 3, 11, and 14) are evaluated. Based on only the large sectors, the first pass of iris authentication may result in a certain number (e.g., M) of hits. That is, M iris data records out of the entire collection of known iris data records may be identified as potentially matching the iris of the user… if a match in the sector identifiers is found, then the second step of the authentication operation is performed. In particular, CV features are computed for the selected sectors of the iris to be authenticated. Then, the CV features of the selected sectors are compared to the CV features of the corresponding select sectors of registered iris data records. If the CV features of the select sectors of the user's iris match those of a registered iris data record, then the user is authenticated. Otherwise, the user is not authenticated. While the above describes iris authentication based on one iris, similar techniques may be extended to implement iris authentication based on both irises of the user, as mentioned previously. --. in [0251]-[0258]).
Re Claim 8, KAEHLER discloses a device, comprising: a camera configured to capture images of an eye (see KAEHLER: e. g., Fig. 1A, and, -- For a given camera resolution, an iris image taken when the iris is expanded (e.g., due to increased levels of blue light) and the pupil is constricted will have higher resolution than an image taken when the iris is constricted (and the pupil expanded), because the expanded iris presents a greater area of the iris to the camera. More iris features can be obtained from such an image and better quality iris codes can be generated from such images.--, in [0022]); and
a controller comprising one or more processors (see KAEHLER: e. g., --under control of a hardware computer processor. The method comprises adjusting a level of blue light, receiving an eye image of an eye exposed to the adjusted level of blue light, detecting a change in a pupillary response by comparison of the received eye image to a reference image, determining that the pupillary response corresponds to a biometric characteristic of a human individual, and allowing access to a biometric application based on the pupillary response determination. The method can be performed by a head mounted display system that includes a processor configured to identify a human individual.--, in [0005]-[0006]) configured to:
cause a first image of the eye to be captured at a first time under a first set of conditions when the eye is an identified eye (see KAEHLER: e. g., --under control of a hardware computer processor. The method comprises adjusting a level of blue light, receiving an eye image of an eye exposed to the adjusted level of blue light, detecting a change in a pupillary response by comparison of the received eye image to a reference image, determining that the pupillary response corresponds to a biometric characteristic of a human individual, and allowing access to a biometric application based on the pupillary response determination. The method can be performed by a head mounted display system that includes a processor configured to identify a human individual.--, in [0005]-[0006]);
transform the first image into a first feature representation for the identified eye (see KAEHLER: e. g., Fig. 1A, and, -- For a given camera resolution, an iris image taken when the iris is expanded (e.g., due to increased levels of blue light) and the pupil is constricted will have higher resolution than an image taken when the iris is constricted (and the pupil expanded), because the expanded iris presents a greater area of the iris to the camera. More iris features can be obtained from such an image and better quality iris codes can be generated from such images.--, in [0022]);
cause a second image of the identified eye to be captured at a second time after the first time under a second set of conditions different than the first set of conditions (see KAEHLER: e. g., --[0075] FIG. 7 schematically illustrates an example pupillary response to light adjustment. In addition to the construction and dilation of the pupil as described above with respect to the example of an eye experiencing blue light adjustment, other physiological characteristics of the eye may be affected by the exposure of an increased level of light exposed to the eye 102…[0076] The pupillary response (e.g., pupil parameter, change in a pupil parameter) can include a variety of physiological characteristics, including but not limited to a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased level of light, a rise curve for the rise time, or a decay curve for the decay time…. eye images obtained from imaging system 500 may be used to form a model of the pupillary response. The model may be based on any parameter derived or measured from an eye image, including but not limited to: the pupil area, the pupil radius, the pupil circumference, the pupil diameter, or the outer iris radius relative to the pupil radius. Additionally, or alternatively, physiological characteristics of the pupillary response may be measured by various instruments coupled to the wearable display system 100 or derived from processing eye images. FIG. 7 schematically illustrates parameters representative of a pupillary response based on an eye image analysis of the processing modules 70, 72.--, in [0075]-[0077], and [0080]; and, --For example, processing modules 70, 72 may create an individual biometric model for an individual utilizing the wearable display system 100. To create this individual biometric model, information obtained from eye images may be used to contribute to that model, including but not limited to: a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased and/or decreased level of light, a rise curve for the rise time, or a decay curve for the decay time, a rise time for a pupillary response curve to the adjusted level of blue light, a decay time for the pupillary response curve to the adjusted level of blue light, a delay time to the adjusted level of blue light, a rise curve portion of the pupillary response curve to the adjusted level of blue light, or a decay curve portion of the pupillary response curve to the adjusted level of blue light. Accordingly, the individual biometric model may include a pupillary response under normal light conditions (e.g., ambient lighting conditions) and pupillary response under an adjusted level of blue light. The individual biometric model may also include reference eye images, such as eye images obtained under normal lighting conditions.--, in [0080], [0083]-[0084], and, --[0089] Once constructed, an individual biometric model may be stored in a biometric database. For example, processing modules 70, 72 may communicate via secure communication channel (e.g., the channel is encrypted) with a server hosting a biometric database. Biometric database may store the individual biometric model as a data record corresponding to that specific individual. In this way, the biometric database may store several biometric models obtained from various wearable display systems 100. Additionally, or alternatively, the individual biometric model may be stored locally (e.g., processing module 70). In such a case, the locally stored individual biometric model may be used for identification of an individual utilizing the wearable display system 100. For example, the wearable display system 100 may only allow access or partial access to an individual that matches the locally stored individual biometric model.--. In [0089]-[0091]);
transform the second image into a second feature representation for the identified eye (see KAEHLER: e. g., Fig. 1A, and, -- For a given camera resolution, an iris image taken when the iris is expanded (e.g., due to increased levels of blue light) and the pupil is constricted will have higher resolution than an image taken when the iris is constricted (and the pupil expanded), because the expanded iris presents a greater area of the iris to the camera. More iris features can be obtained from such an image and better quality iris codes can be generated from such images.--, in [0022]);
configure a multi-representation eye model for the identified eye, wherein to configure the multi-representation eye model for the identified eye the controller (see KAEHLER: e. g., --[0075] FIG. 7 schematically illustrates an example pupillary response to light adjustment. In addition to the construction and dilation of the pupil as described above with respect to the example of an eye experiencing blue light adjustment, other physiological characteristics of the eye may be affected by the exposure of an increased level of light exposed to the eye 102…[0076] The pupillary response (e.g., pupil parameter, change in a pupil parameter) can include a variety of physiological characteristics, including but not limited to a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased level of light, a rise curve for the rise time, or a decay curve for the decay time…. eye images obtained from imaging system 500 may be used to form a model of the pupillary response. The model may be based on any parameter derived or measured from an eye image, including but not limited to: the pupil area, the pupil radius, the pupil circumference, the pupil diameter, or the outer iris radius relative to the pupil radius. Additionally, or alternatively, physiological characteristics of the pupillary response may be measured by various instruments coupled to the wearable display system 100 or derived from processing eye images. FIG. 7 schematically illustrates parameters representative of a pupillary response based on an eye image analysis of the processing modules 70, 72.--, in [0075]-[0077], and [0080]; and, --For example, processing modules 70, 72 may create an individual biometric model for an individual utilizing the wearable display system 100. To create this individual biometric model, information obtained from eye images may be used to contribute to that model, including but not limited to: a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased and/or decreased level of light, a rise curve for the rise time, or a decay curve for the decay time, a rise time for a pupillary response curve to the adjusted level of blue light, a decay time for the pupillary response curve to the adjusted level of blue light, a delay time to the adjusted level of blue light, a rise curve portion of the pupillary response curve to the adjusted level of blue light, or a decay curve portion of the pupillary response curve to the adjusted level of blue light. Accordingly, the individual biometric model may include a pupillary response under normal light conditions (e.g., ambient lighting conditions) and pupillary response under an adjusted level of blue light. The individual biometric model may also include reference eye images, such as eye images obtained under normal lighting conditions.--, in [0080], [0083]-[0084], and, --[0089] Once constructed, an individual biometric model may be stored in a biometric database. For example, processing modules 70, 72 may communicate via secure communication channel (e.g., the channel is encrypted) with a server hosting a biometric database. Biometric database may store the individual biometric model as a data record corresponding to that specific individual. In this way, the biometric database may store several biometric models obtained from various wearable display systems 100. Additionally, or alternatively, the individual biometric model may be stored locally (e.g., processing module 70). In such a case, the locally stored individual biometric model may be used for identification of an individual utilizing the wearable display system 100. For example, the wearable display system 100 may only allow access or partial access to an individual that matches the locally stored individual biometric model.--. In [0089]-[0091]) is configured to:
generate, based on at least the first feature representation and the second feature representation, the multi-representation eye model for the identified eye (see KAEHLER: e. g., --[0075] FIG. 7 schematically illustrates an example pupillary response to light adjustment. In addition to the construction and dilation of the pupil as described above with respect to the example of an eye experiencing blue light adjustment, other physiological characteristics of the eye may be affected by the exposure of an increased level of light exposed to the eye 102…[0076] The pupillary response (e.g., pupil parameter, change in a pupil parameter) can include a variety of physiological characteristics, including but not limited to a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased level of light, a rise curve for the rise time, or a decay curve for the decay time…. eye images obtained from imaging system 500 may be used to form a model of the pupillary response. The model may be based on any parameter derived or measured from an eye image, including but not limited to: the pupil area, the pupil radius, the pupil circumference, the pupil diameter, or the outer iris radius relative to the pupil radius. Additionally, or alternatively, physiological characteristics of the pupillary response may be measured by various instruments coupled to the wearable display system 100 or derived from processing eye images. FIG. 7 schematically illustrates parameters representative of a pupillary response based on an eye image analysis of the processing modules 70, 72.--, in [0075]-[0077], and [0080]; and, --For example, processing modules 70, 72 may create an individual biometric model for an individual utilizing the wearable display system 100. To create this individual biometric model, information obtained from eye images may be used to contribute to that model, including but not limited to: a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased and/or decreased level of light, a rise curve for the rise time, or a decay curve for the decay time, a rise time for a pupillary response curve to the adjusted level of blue light, a decay time for the pupillary response curve to the adjusted level of blue light, a delay time to the adjusted level of blue light, a rise curve portion of the pupillary response curve to the adjusted level of blue light, or a decay curve portion of the pupillary response curve to the adjusted level of blue light. Accordingly, the individual biometric model may include a pupillary response under normal light conditions (e.g., ambient lighting conditions) and pupillary response under an adjusted level of blue light. The individual biometric model may also include reference eye images, such as eye images obtained under normal lighting conditions.--, in [0080], [0083]-[0084], and, --[0089] Once constructed, an individual biometric model may be stored in a biometric database. For example, processing modules 70, 72 may communicate via secure communication channel (e.g., the channel is encrypted) with a server hosting a biometric database. Biometric database may store the individual biometric model as a data record corresponding to that specific individual. In this way, the biometric database may store several biometric models obtained from various wearable display systems 100. Additionally, or alternatively, the individual biometric model may be stored locally (e.g., processing module 70). In such a case, the locally stored individual biometric model may be used for identification of an individual utilizing the wearable display system 100. For example, the wearable display system 100 may only allow access or partial access to an individual that matches the locally stored individual biometric model.--. In [0089]-[0091]), or
KAEHLER however does not explicitly disclose successively update the multi-representation eye model for the identified eye based on the first feature representation and the second feature representation;
GOUSEV discloses successively update the multi-representation eye model for the identified eye based on the first feature representation and the second feature representation (see GOUSEV: e.g., --Cascade classifier hardware 244 can, in some implementations, be considered dedicated cascade classifier hardware in the sense that it is hardware designed to perform the cascade classifier function and little to no other significant functions. While the implementation described above relates to a cascade classifier based on programmed weights and thresholds based on previous, in the laboratory, training and machine learning to generate a model, it is understood that cascade classifier hardware 244, or other hardware in peripheral circuitry designed to perform CV operations based on hardware-computed CV features received from CV computation hardware 242, can be designed to perform machine learning in the field.--, in [0087]; -- performance of iris-related operations, particularly with respect to power consumption. FIG. 12 shows an embodiment using two different types of sensors—a visual sensor 1202 and an IR sensor 1206. Example implementations of visual sensor 1202 can include sensor system 210 of FIG. 2A, sensor system of FIG. 2B, or visual sensor system 1310 of FIG. 13. Although visual sensor 1202 and IR sensor 1206 are illustrated as two separate sensors, in some implementations described further below, it is understood that CV computation hardware within visual sensor 1202 can perform CV feature computation based on IR images captured by IR sensor 1206. In such implementations, no opening in the front of the phone will be necessary for visual sensor 1202 as both events for activating iris scanning or other iris processing as well as the iris scanning or processing itself can be based on processing of IR images from the IR sensor 1206. … mobile device 1200 may be transitioned to a high-power state to perform iris-related operations, according to this embodiment. The IR light source 1204 may be turned on, and the IR sensor 1206 may be used to capture images of the surroundings illuminated by the IR light source 1204. Images captured by the IR sensor 1206 may be used for iris-related tasks such as iris detection, iris authentication. --, in [0146]-[0147], and, Fig. 14, Fig. 15, and Fig. 16; --[0179] The landmark detector model may comprise the following components: [0180] s.sub.R,μ=mean shape vector, to be interpreted as a coarse shape estimate, with respect to a reference bounding box (denoted by suffix R, unit square with top left corner at 0). [0181] Corresponding to regressor stage m=1 to M: [0182] SRET.sup.(m): Shape Relative Encoding Table storing the relative locations of 512 feature pixels w.r.t. current shape estimate. [0183] Table size is 512 rows×16 bits—from MSB to LSB, first two bits index one of the eyes' four landmarks' locations (00, 01, 10, 11 correspond to the left eye outer, left eye inner, right eye outer, and right eye inner landmarks, respectively), the next seven bits correspond to the relative offset of feature pixel location along the rows, and the final seven bits correspond to the same offset along the column dimension. [0184] FIT.sup.(m): Feature Index Table storing information about the specific pairs of pixels that would need to be consumed by each of the 256 decision stumps comprising regressor stage m, i.e., each decision stump would take in as inputs the feature intensities at the locations encoded by the first 16 bits of FIT.sup.(m), and compare the intensity difference to a threshold as encoded by the last 8 bits in the entries of FIT.sup.(m). [0185] Table size is 256 rows×24 bits—8 bits for index corresponding to feature pixel location 1, 8 bits for index corresponding to feature pixel location 2, and 8 bits for the value of threshold used in the decision tree stump that consumes these two feature pixel intensities. [0186] LNT.sup.(m): Leaf Node Table storing the shape correction vector at each of the 512 leaf nodes in regressor stage m. [0187] Table size is 512 rows×32 bits—each leaf node entry corresponds to an 8-dimensional shape correction vector. [0188] α.sup.(m): Scalar that controls the dynamic range of the leaf-node entries in LNT.sup.(m)
[0189] Outputs of the landmark detector may consist of the locations of four eye landmarks, i.e., shape estimates s.sub.1∈R.sup.8, corresponding to the four corners of the eyes of the user. Examples of such landmarks may be the landmarks 1602, 1604, 1606, and 1608 shown in FIG. 16. Here, the shape vector is an eight-dimensional vector (two coordinates to describe each of the four eye landmarks).--, in [0178]-[0189], and [0216], and, -- [0251] Just as an example, in a “two-pass” iris authentication scheme, a “first pass” of iris authentication may focus on large sectors such as sectors 3, 11, and 14. The first pass of iris authentication may involve comparison against the entire collection of known iris data records. However, the burden of performing such a large number of comparisons is offset by the fact that for each comparison, only a few sectors (i.e., large sectors 3, 11, and 14) are evaluated. Based on only the large sectors, the first pass of iris authentication may result in a certain number (e.g., M) of hits. That is, M iris data records out of the entire collection of known iris data records may be identified as potentially matching the iris of the user… if a match in the sector identifiers is found, then the second step of the authentication operation is performed. In particular, CV features are computed for the selected sectors of the iris to be authenticated. Then, the CV features of the selected sectors are compared to the CV features of the corresponding select sectors of registered iris data records. If the CV features of the select sectors of the user's iris match those of a registered iris data record, then the user is authenticated. Otherwise, the user is not authenticated. While the above describes iris authentication based on one iris, similar techniques may be extended to implement iris authentication based on both irises of the user, as mentioned previously. --. in [0251]-[0258]);
KAEHLER and GOUSEV are combinable as they are in the same field of endeavor: using captured eye images and features in authentication. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify KAEHLER’s device using GOUSEV’s teachings by including successively update the multi-representation eye model for the identified eye based on the first feature representation and the second feature representation to KAEHLER’s generating eye model in order to determine the results of match those of a registered iris data record (see GOUSEV: e.g., in [0003], [0146]-[0147], [0178]-[0189], [0216], and [0251]-[0258]);
KAEHLER as modified by GOUSEV further disclose store the multi-representation eye model for use during a user authentication process (see KAEHLER: e. g., --[0075] FIG. 7 schematically illustrates an example pupillary response to light adjustment. In addition to the construction and dilation of the pupil as described above with respect to the example of an eye experiencing blue light adjustment, other physiological characteristics of the eye may be affected by the exposure of an increased level of light exposed to the eye 102…[0076] The pupillary response (e.g., pupil parameter, change in a pupil parameter) can include a variety of physiological characteristics, including but not limited to a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased level of light, a rise curve for the rise time, or a decay curve for the decay time…. eye images obtained from imaging system 500 may be used to form a model of the pupillary response. The model may be based on any parameter derived or measured from an eye image, including but not limited to: the pupil area, the pupil radius, the pupil circumference, the pupil diameter, or the outer iris radius relative to the pupil radius. Additionally, or alternatively, physiological characteristics of the pupillary response may be measured by various instruments coupled to the wearable display system 100 or derived from processing eye images. FIG. 7 schematically illustrates parameters representative of a pupillary response based on an eye image analysis of the processing modules 70, 72.--, in [0075]-[0077], and [0080]; and, --For example, processing modules 70, 72 may create an individual biometric model for an individual utilizing the wearable display system 100. To create this individual biometric model, information obtained from eye images may be used to contribute to that model, including but not limited to: a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased and/or decreased level of light, a rise curve for the rise time, or a decay curve for the decay time, a rise time for a pupillary response curve to the adjusted level of blue light, a decay time for the pupillary response curve to the adjusted level of blue light, a delay time to the adjusted level of blue light, a rise curve portion of the pupillary response curve to the adjusted level of blue light, or a decay curve portion of the pupillary response curve to the adjusted level of blue light. Accordingly, the individual biometric model may include a pupillary response under normal light conditions (e.g., ambient lighting conditions) and pupillary response under an adjusted level of blue light. The individual biometric model may also include reference eye images, such as eye images obtained under normal lighting conditions.--, in [0080], [0083]-[0084], and, --[0089] Once constructed, an individual biometric model may be stored in a biometric database. For example, processing modules 70, 72 may communicate via secure communication channel (e.g., the channel is encrypted) with a server hosting a biometric database. Biometric database may store the individual biometric model as a data record corresponding to that specific individual. In this way, the biometric database may store several biometric models obtained from various wearable display systems 100. Additionally, or alternatively, the individual biometric model may be stored locally (e.g., processing module 70). In such a case, the locally stored individual biometric model may be used for identification of an individual utilizing the wearable display system 100. For example, the wearable display system 100 may only allow access or partial access to an individual that matches the locally stored individual biometric model.--. In [0089]-[0091]).
Re Claim 9, KAEHLER as modified by GOUSEV further disclose wherein the processors are configured to initiate an enrollment process for eye authentication for a user, wherein the first image and the second image are captured as part of the enrollment process and the controller generates the multi-representation eye model as part of the enrollment process based on at least the first feature representation and the second feature representation (see KAEHLER: e. g., --[0126] (5) The embodiment in any of (1) to (4), in which the processing system identifies a high degree of confidence is needed in iris code construction. For example, a high degree of confidence may be needed for original enrollment in a biometric system or when a financial transaction is being made. That is, the degree of confidence for original enrollment in a biometric system or such a financial transaction passes a confidence threshold.--, in [0126]; also see Gousev: e.g., --iris authentication involves (1) obtaining an iris data record of the user to be authenticated and (2) comparing the iris data record of the user to one or more registered iris data records, to authenticate the user. The iris data record is derived from the image of the iris. The iris data record may be generated from the image of the iris in many different ways. Various techniques may be employed individually or in combination, such as windowing, computer vision (CV) feature computation, checksums, digests, hash functions, etc. Regardless of how the iris data record is generated, embodiments of the present disclosure utilizes the natural pupillary response of the eye to adjust the shape of the iris prior to image capture, in order to provide iris shape normalization and improve the accuracy and efficiency of iris authentication.
[0221] In various embodiments of the present disclosure, the visible light source may be mounted on a device such as mobile device 1200 and oriented toward the user. The light source may be capable of outputting visible light of a controllable intensity. Here, the light source may output light in the visible spectrum for purposes of controlling pupil size, because the human pupil typically contracts and dilates in response to different intensities of light in the visible spectrum.
[0222] While visible light is used to control the shape of the iris through the effects of pupillary response, the image captured of the eye may or may not involve use of IR illumination. IR light is often used to highlight internal features and details of the iris when capturing an image for purposes of iris authentication. Embodiments of the present disclosure may be implemented flexibly, either with or without IR illumination. In one example, no IR illumination is used.--, in [0220]-[0222]).
Re Claim 11, KAEHLER as modified by GOUSEV further disclose wherein the device further comprises a display configured to be positioned in front of the eye, wherein the controller is configured to control the display to at least partially create the first set of conditions for capturing the first image and the second set of conditions for capturing the second image (see KAEHLER: e. g., --[0075] FIG. 7 schematically illustrates an example pupillary response to light adjustment. In addition to the construction and dilation of the pupil as described above with respect to the example of an eye experiencing blue light adjustment, other physiological characteristics of the eye may be affected by the exposure of an increased level of light exposed to the eye 102…[0076] The pupillary response (e.g., pupil parameter, change in a pupil parameter) can include a variety of physiological characteristics, including but not limited to a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased level of light, a rise curve for the rise time, or a decay curve for the decay time…. eye images obtained from imaging system 500 may be used to form a model of the pupillary response. The model may be based on any parameter derived or measured from an eye image, including but not limited to: the pupil area, the pupil radius, the pupil circumference, the pupil diameter, or the outer iris radius relative to the pupil radius. Additionally, or alternatively, physiological characteristics of the pupillary response may be measured by various instruments coupled to the wearable display system 100 or derived from processing eye images. FIG. 7 schematically illustrates parameters representative of a pupillary response based on an eye image analysis of the processing modules 70, 72.--, in [0075]-[0077], and [0080]; and, --For example, processing modules 70, 72 may create an individual biometric model for an individual utilizing the wearable display system 100. To create this individual biometric model, information obtained from eye images may be used to contribute to that model, including but not limited to: a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased and/or decreased level of light, a rise curve for the rise time, or a decay curve for the decay time, a rise time for a pupillary response curve to the adjusted level of blue light, a decay time for the pupillary response curve to the adjusted level of blue light, a delay time to the adjusted level of blue light, a rise curve portion of the pupillary response curve to the adjusted level of blue light, or a decay curve portion of the pupillary response curve to the adjusted level of blue light. Accordingly, the individual biometric model may include a pupillary response under normal light conditions (e.g., ambient lighting conditions) and pupillary response under an adjusted level of blue light. The individual biometric model may also include reference eye images, such as eye images obtained under normal lighting conditions.--, in [0080], [0083]-[0084], and, --[0089] Once constructed, an individual biometric model may be stored in a biometric database. For example, processing modules 70, 72 may communicate via secure communication channel (e.g., the channel is encrypted) with a server hosting a biometric database. Biometric database may store the individual biometric model as a data record corresponding to that specific individual. In this way, the biometric database may store several biometric models obtained from various wearable display systems 100. Additionally, or alternatively, the individual biometric model may be stored locally (e.g., processing module 70). In such a case, the locally stored individual biometric model may be used for identification of an individual utilizing the wearable display system 100. For example, the wearable display system 100 may only allow access or partial access to an individual that matches the locally stored individual biometric model.--. In [0089]-[0091]).
Re Claim 12, KAEHLER as modified by GOUSEV further disclose change the brightness of the display to provide a different brightness for the second set of conditions than the first set of conditions, or change a depth appearance of an object on the display to provide a different accommodation distance for the second set of conditions than the first set of conditions (see KAEHLER: e. g., --[0075] FIG. 7 schematically illustrates an example pupillary response to light adjustment. In addition to the construction and dilation of the pupil as described above with respect to the example of an eye experiencing blue light adjustment, other physiological characteristics of the eye may be affected by the exposure of an increased level of light exposed to the eye 102…[0076] The pupillary response (e.g., pupil parameter, change in a pupil parameter) can include a variety of physiological characteristics, including but not limited to a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased level of light, a rise curve for the rise time, or a decay curve for the decay time…. eye images obtained from imaging system 500 may be used to form a model of the pupillary response. The model may be based on any parameter derived or measured from an eye image, including but not limited to: the pupil area, the pupil radius, the pupil circumference, the pupil diameter, or the outer iris radius relative to the pupil radius. Additionally, or alternatively, physiological characteristics of the pupillary response may be measured by various instruments coupled to the wearable display system 100 or derived from processing eye images. FIG. 7 schematically illustrates parameters representative of a pupillary response based on an eye image analysis of the processing modules 70, 72.--, in [0075]-[0077], and [0080]; and, --For example, processing modules 70, 72 may create an individual biometric model for an individual utilizing the wearable display system 100. To create this individual biometric model, information obtained from eye images may be used to contribute to that model, including but not limited to: a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased and/or decreased level of light, a rise curve for the rise time, or a decay curve for the decay time, a rise time for a pupillary response curve to the adjusted level of blue light, a decay time for the pupillary response curve to the adjusted level of blue light, a delay time to the adjusted level of blue light, a rise curve portion of the pupillary response curve to the adjusted level of blue light, or a decay curve portion of the pupillary response curve to the adjusted level of blue light. Accordingly, the individual biometric model may include a pupillary response under normal light conditions (e.g., ambient lighting conditions) and pupillary response under an adjusted level of blue light. The individual biometric model may also include reference eye images, such as eye images obtained under normal lighting conditions.--, in [0080], [0083]-[0084], and, --[0089] Once constructed, an individual biometric model may be stored in a biometric database. For example, processing modules 70, 72 may communicate via secure communication channel (e.g., the channel is encrypted) with a server hosting a biometric database. Biometric database may store the individual biometric model as a data record corresponding to that specific individual. In this way, the biometric database may store several biometric models obtained from various wearable display systems 100. Additionally, or alternatively, the individual biometric model may be stored locally (e.g., processing module 70). In such a case, the locally stored individual biometric model may be used for identification of an individual utilizing the wearable display system 100. For example, the wearable display system 100 may only allow access or partial access to an individual that matches the locally stored individual biometric model.--. In [0089]-[0091]; and, --an unauthorized individual may attempt to gain access to the display system and/or system application by imitating (also known as spoofing) the biometric characteristics of an actual authorized user. For example, an unauthorized individual seeking to achieve illicit access to wearable display system may present to the display system a picture (or a 3D model) of the iris of the authorized user. One system application may be projecting images onto the display for a user as if the images appear at different distances from the user. Other examples may include software that allow a user to engage with the Internet, software applications (“apps”), system settings, system security features, etc. The display system may image the iris picture or model and be fooled into permitting access to the unauthorized individual. As another example of improving the security of the wearable display, start-up routines for a wearable display system may incorporate a scene that stimulates the pupillary response. For example, as part of a startup routine that initiates the wearable display system, the display can project an image of a sunrise, where the sky increasingly becomes more blue. Such a scene can stimulate or trigger the pupillary response of the individual wearing the system, so that the wearable display can measure the wearer's pupillary response (e.g., in response to the bluing of the sky) and use the measured response to identify the wearer--, in [0092]).
Re Claim 14, KAEHLER as modified by GOUSEV further disclose wherein to generate or update the multi representation eye model the controller is configured to train a machine learning model for the multi-representation eye model using the first feature representation and the second feature representation (see GOUSEV: e.g., --Cascade classifier hardware 244 can, in some implementations, be considered dedicated cascade classifier hardware in the sense that it is hardware designed to perform the cascade classifier function and little to no other significant functions. While the implementation described above relates to a cascade classifier based on programmed weights and thresholds based on previous, in the laboratory, training and machine learning to generate a model, it is understood that cascade classifier hardware 244, or other hardware in peripheral circuitry designed to perform CV operations based on hardware-computed CV features received from CV computation hardware 242, can be designed to perform machine learning in the field.--, in [0087]; -- performance of iris-related operations, particularly with respect to power consumption. FIG. 12 shows an embodiment using two different types of sensors—a visual sensor 1202 and an IR sensor 1206. Example implementations of visual sensor 1202 can include sensor system 210 of FIG. 2A, sensor system of FIG. 2B, or visual sensor system 1310 of FIG. 13. Although visual sensor 1202 and IR sensor 1206 are illustrated as two separate sensors, in some implementations described further below, it is understood that CV computation hardware within visual sensor 1202 can perform CV feature computation based on IR images captured by IR sensor 1206. In such implementations, no opening in the front of the phone will be necessary for visual sensor 1202 as both events for activating iris scanning or other iris processing as well as the iris scanning or processing itself can be based on processing of IR images from the IR sensor 1206. … mobile device 1200 may be transitioned to a high-power state to perform iris-related operations, according to this embodiment. The IR light source 1204 may be turned on, and the IR sensor 1206 may be used to capture images of the surroundings illuminated by the IR light source 1204. Images captured by the IR sensor 1206 may be used for iris-related tasks such as iris detection, iris authentication. --, in [0146]-[0147], and, Fig. 14, Fig. 15, and Fig. 16; --[0179] The landmark detector model may comprise the following components: [0180] s.sub.R,μ=mean shape vector, to be interpreted as a coarse shape estimate, with respect to a reference bounding box (denoted by suffix R, unit square with top left corner at 0). [0181] Corresponding to regressor stage m=1 to M: [0182] SRET.sup.(m): Shape Relative Encoding Table storing the relative locations of 512 feature pixels w.r.t. current shape estimate. [0183] Table size is 512 rows×16 bits—from MSB to LSB, first two bits index one of the eyes' four landmarks' locations (00, 01, 10, 11 correspond to the left eye outer, left eye inner, right eye outer, and right eye inner landmarks, respectively), the next seven bits correspond to the relative offset of feature pixel location along the rows, and the final seven bits correspond to the same offset along the column dimension. [0184] FIT.sup.(m): Feature Index Table storing information about the specific pairs of pixels that would need to be consumed by each of the 256 decision stumps comprising regressor stage m, i.e., each decision stump would take in as inputs the feature intensities at the locations encoded by the first 16 bits of FIT.sup.(m), and compare the intensity difference to a threshold as encoded by the last 8 bits in the entries of FIT.sup.(m). [0185] Table size is 256 rows×24 bits—8 bits for index corresponding to feature pixel location 1, 8 bits for index corresponding to feature pixel location 2, and 8 bits for the value of threshold used in the decision tree stump that consumes these two feature pixel intensities. [0186] LNT.sup.(m): Leaf Node Table storing the shape correction vector at each of the 512 leaf nodes in regressor stage m. [0187] Table size is 512 rows×32 bits—each leaf node entry corresponds to an 8-dimensional shape correction vector. [0188] α.sup.(m): Scalar that controls the dynamic range of the leaf-node entries in LNT.sup.(m)
[0189] Outputs of the landmark detector may consist of the locations of four eye landmarks, i.e., shape estimates s.sub.1∈R.sup.8, corresponding to the four corners of the eyes of the user. Examples of such landmarks may be the landmarks 1602, 1604, 1606, and 1608 shown in FIG. 16. Here, the shape vector is an eight-dimensional vector (two coordinates to describe each of the four eye landmarks).--, in [0178]-[0189]).
Re Claim 15, KAEHLER as modified by GOUSEV further disclose wherein to generate or update the multi representation eye model the controller is configured to separately store the first feature representation and the second feature representation as part of the multi-representation eye model (see GOUSEV: e.g., -- [0251] Just as an example, in a “two-pass” iris authentication scheme, a “first pass” of iris authentication may focus on large sectors such as sectors 3, 11, and 14. The first pass of iris authentication may involve comparison against the entire collection of known iris data records. However, the burden of performing such a large number of comparisons is offset by the fact that for each comparison, only a few sectors (i.e., large sectors 3, 11, and 14) are evaluated. Based on only the large sectors, the first pass of iris authentication may result in a certain number (e.g., M) of hits. That is, M iris data records out of the entire collection of known iris data records may be identified as potentially matching the iris of the user… if a match in the sector identifiers is found, then the second step of the authentication operation is performed. In particular, CV features are computed for the selected sectors of the iris to be authenticated. Then, the CV features of the selected sectors are compared to the CV features of the corresponding select sectors of registered iris data records. If the CV features of the select sectors of the user's iris match those of a registered iris data record, then the user is authenticated. Otherwise, the user is not authenticated. While the above describes iris authentication based on one iris, similar techniques may be extended to implement iris authentication based on both irises of the user, as mentioned previously. --. in [0251]-[0258]).
Re Claim 16, KAEHLER as modified by GOUSEV further disclose determine a new set of conditions affecting the identified eye (see KAEHLER: e. g., Fig. 1A, and, -- For a given camera resolution, an iris image taken when the iris is expanded (e.g., due to increased levels of blue light) and the pupil is constricted will have higher resolution than an image taken when the iris is constricted (and the pupil expanded), because the expanded iris presents a greater area of the iris to the camera. More iris features can be obtained from such an image and better quality iris codes can be generated from such images.--, in [0022]);
determine whether the new set of conditions are sufficiently represented in the multi-representation eye model (see KAEHLER: e. g., Fig. 1A, and, -- For a given camera resolution, an iris image taken when the iris is expanded (e.g., due to increased levels of blue light) and the pupil is constricted will have higher resolution than an image taken when the iris is constricted (and the pupil expanded), because the expanded iris presents a greater area of the iris to the camera. More iris features can be obtained from such an image and better quality iris codes can be generated from such images.--, in [0022]);
capture, based on a determination that the new set of conditions are not sufficiently represented in the multi-representation eye model, a new image of the identified eye under the new set of conditions (see KAEHLER: e. g., Fig. 1A, and, -- For a given camera resolution, an iris image taken when the iris is expanded (e.g., due to increased levels of blue light) and the pupil is constricted will have higher resolution than an image taken when the iris is constricted (and the pupil expanded), because the expanded iris presents a greater area of the iris to the camera. More iris features can be obtained from such an image and better quality iris codes can be generated from such images.--, in [0022]; --[0075] FIG. 7 schematically illustrates an example pupillary response to light adjustment. In addition to the construction and dilation of the pupil as described above with respect to the example of an eye experiencing blue light adjustment, other physiological characteristics of the eye may be affected by the exposure of an increased level of light exposed to the eye 102…[0076] The pupillary response (e.g., pupil parameter, change in a pupil parameter) can include a variety of physiological characteristics, including but not limited to a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased level of light, a rise curve for the rise time, or a decay curve for the decay time…. eye images obtained from imaging system 500 may be used to form a model of the pupillary response. The model may be based on any parameter derived or measured from an eye image, including but not limited to: the pupil area, the pupil radius, the pupil circumference, the pupil diameter, or the outer iris radius relative to the pupil radius. Additionally, or alternatively, physiological characteristics of the pupillary response may be measured by various instruments coupled to the wearable display system 100 or derived from processing eye images. FIG. 7 schematically illustrates parameters representative of a pupillary response based on an eye image analysis of the processing modules 70, 72.--, in [0075]-[0077], and [0080]; and, --For example, processing modules 70, 72 may create an individual biometric model for an individual utilizing the wearable display system 100. To create this individual biometric model, information obtained from eye images may be used to contribute to that model, including but not limited to: a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased and/or decreased level of light, a rise curve for the rise time, or a decay curve for the decay time, a rise time for a pupillary response curve to the adjusted level of blue light, a decay time for the pupillary response curve to the adjusted level of blue light, a delay time to the adjusted level of blue light, a rise curve portion of the pupillary response curve to the adjusted level of blue light, or a decay curve portion of the pupillary response curve to the adjusted level of blue light. Accordingly, the individual biometric model may include a pupillary response under normal light conditions (e.g., ambient lighting conditions) and pupillary response under an adjusted level of blue light. The individual biometric model may also include reference eye images, such as eye images obtained under normal lighting conditions.--, in [0080], [0083]-[0084], and, --[0089] Once constructed, an individual biometric model may be stored in a biometric database. For example, processing modules 70, 72 may communicate via secure communication channel (e.g., the channel is encrypted) with a server hosting a biometric database. Biometric database may store the individual biometric model as a data record corresponding to that specific individual. In this way, the biometric database may store several biometric models obtained from various wearable display systems 100. Additionally, or alternatively, the individual biometric model may be stored locally (e.g., processing module 70). In such a case, the locally stored individual biometric model may be used for identification of an individual utilizing the wearable display system 100. For example, the wearable display system 100 may only allow access or partial access to an individual that matches the locally stored individual biometric model.--. In [0089]-[0091]);
transform the new image into a new feature representation for the identified eye; and update the multi-representation eye model for the identified eye based on the new feature representation (see KAEHLER: e. g., Fig. 1A, and, -- For a given camera resolution, an iris image taken when the iris is expanded (e.g., due to increased levels of blue light) and the pupil is constricted will have higher resolution than an image taken when the iris is constricted (and the pupil expanded), because the expanded iris presents a greater area of the iris to the camera. More iris features can be obtained from such an image and better quality iris codes can be generated from such images.--, in [0022]; --[0075] FIG. 7 schematically illustrates an example pupillary response to light adjustment. In addition to the construction and dilation of the pupil as described above with respect to the example of an eye experiencing blue light adjustment, other physiological characteristics of the eye may be affected by the exposure of an increased level of light exposed to the eye 102…[0076] The pupillary response (e.g., pupil parameter, change in a pupil parameter) can include a variety of physiological characteristics, including but not limited to a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased level of light, a rise curve for the rise time, or a decay curve for the decay time…. eye images obtained from imaging system 500 may be used to form a model of the pupillary response. The model may be based on any parameter derived or measured from an eye image, including but not limited to: the pupil area, the pupil radius, the pupil circumference, the pupil diameter, or the outer iris radius relative to the pupil radius. Additionally, or alternatively, physiological characteristics of the pupillary response may be measured by various instruments coupled to the wearable display system 100 or derived from processing eye images. FIG. 7 schematically illustrates parameters representative of a pupillary response based on an eye image analysis of the processing modules 70, 72.--, in [0075]-[0077], and [0080]; and, --For example, processing modules 70, 72 may create an individual biometric model for an individual utilizing the wearable display system 100. To create this individual biometric model, information obtained from eye images may be used to contribute to that model, including but not limited to: a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased and/or decreased level of light, a rise curve for the rise time, or a decay curve for the decay time, a rise time for a pupillary response curve to the adjusted level of blue light, a decay time for the pupillary response curve to the adjusted level of blue light, a delay time to the adjusted level of blue light, a rise curve portion of the pupillary response curve to the adjusted level of blue light, or a decay curve portion of the pupillary response curve to the adjusted level of blue light. Accordingly, the individual biometric model may include a pupillary response under normal light conditions (e.g., ambient lighting conditions) and pupillary response under an adjusted level of blue light. The individual biometric model may also include reference eye images, such as eye images obtained under normal lighting conditions.--, in [0080], [0083]-[0084], and, --[0089] Once constructed, an individual biometric model may be stored in a biometric database. For example, processing modules 70, 72 may communicate via secure communication channel (e.g., the channel is encrypted) with a server hosting a biometric database. Biometric database may store the individual biometric model as a data record corresponding to that specific individual. In this way, the biometric database may store several biometric models obtained from various wearable display systems 100. Additionally, or alternatively, the individual biometric model may be stored locally (e.g., processing module 70). In such a case, the locally stored individual biometric model may be used for identification of an individual utilizing the wearable display system 100. For example, the wearable display system 100 may only allow access or partial access to an individual that matches the locally stored individual biometric model.--. In [0089]-[0091]).
Re Claim 17, KAEHLER as modified by GOUSEV further disclose wherein to update the multi-representation eye model for the identified eye based on the new feature representation the controller is configured to replace an existing feature representation of the multi-representation eye model with the new feature representation based on a quality indicator for the existing feature representation or an age of the existing feature representation (see KAEHLER: e. g., Fig. 1A, and, -- For a given camera resolution, an iris image taken when the iris is expanded (e.g., due to increased levels of blue light) and the pupil is constricted will have higher resolution than an image taken when the iris is constricted (and the pupil expanded), because the expanded iris presents a greater area of the iris to the camera. More iris features can be obtained from such an image and better quality iris codes can be generated from such images.--, in [0022]; --[0075] FIG. 7 schematically illustrates an example pupillary response to light adjustment. In addition to the construction and dilation of the pupil as described above with respect to the example of an eye experiencing blue light adjustment, other physiological characteristics of the eye may be affected by the exposure of an increased level of light exposed to the eye 102…[0076] The pupillary response (e.g., pupil parameter, change in a pupil parameter) can include a variety of physiological characteristics, including but not limited to a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased level of light, a rise curve for the rise time, or a decay curve for the decay time…. eye images obtained from imaging system 500 may be used to form a model of the pupillary response. The model may be based on any parameter derived or measured from an eye image, including but not limited to: the pupil area, the pupil radius, the pupil circumference, the pupil diameter, or the outer iris radius relative to the pupil radius. Additionally, or alternatively, physiological characteristics of the pupillary response may be measured by various instruments coupled to the wearable display system 100 or derived from processing eye images. FIG. 7 schematically illustrates parameters representative of a pupillary response based on an eye image analysis of the processing modules 70, 72.--, in [0075]-[0077], and [0080]; and, --For example, processing modules 70, 72 may create an individual biometric model for an individual utilizing the wearable display system 100. To create this individual biometric model, information obtained from eye images may be used to contribute to that model, including but not limited to: a rise time for a pupillary response curve to an increased level of light, a decay time for the pupillary response curve to a decreased level of light, a delay time to an increased and/or decreased level of light, a rise curve for the rise time, or a decay curve for the decay time, a rise time for a pupillary response curve to the adjusted level of blue light, a decay time for the pupillary response curve to the adjusted level of blue light, a delay time to the adjusted level of blue light, a rise curve portion of the pupillary response curve to the adjusted level of blue light, or a decay curve portion of the pupillary response curve to the adjusted level of blue light. Accordingly, the individual biometric model may include a pupillary response under normal light conditions (e.g., ambient lighting conditions) and pupillary response under an adjusted level of blue light. The individual biometric model may also include reference eye images, such as eye images obtained under normal lighting conditions.--, in [0080], [0083]-[0084], and, --[0089] Once constructed, an individual biometric model may be stored in a biometric database. For example, processing modules 70, 72 may communicate via secure communication channel (e.g., the channel is encrypted) with a server hosting a biometric database. Biometric database may store the individual biometric model as a data record corresponding to that specific individual. In this way, the biometric database may store several biometric models obtained from various wearable display systems 100. Additionally, or alternatively, the individual biometric model may be stored locally (e.g., processing module 70). In such a case, the locally stored individual biometric model may be used for identification of an individual utilizing the wearable display system 100. For example, the wearable display system 100 may only allow access or partial access to an individual that matches the locally stored individual biometric model.--. In [0089]-[0091]).
Re Claims 18-19, claims 18-19 are the corresponding method claims to claims 1, and 8 respectively. Claims 18-19 thus are rejected for the similar reasons for claims 1, and 8. See above discussions with regard to claims 1, and 8 respectively. KAEHLER as modified by GOUSEV further disclose a method, comprising: performing, by a controller comprising one or more processors to the steps (see KAEHLER: e. g., --under control of a hardware computer processor. The method comprises adjusting a level of blue light, receiving an eye image of an eye exposed to the adjusted level of blue light, detecting a change in a pupillary response by comparison of the received eye image to a reference image, determining that the pupillary response corresponds to a biometric characteristic of a human individual, and allowing access to a biometric application based on the pupillary response determination. The method can be performed by a head mounted display system that includes a processor configured to identify a human individual.--, in [0005]-[0006]).
Conclusion
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/WEI WEN YANG/Primary Examiner, Art Unit 2662