Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings were received on 01/08/2025. These drawings are accepted.
Claim Objections
Claims 1, 14 and 16 are objected to because of the following informalities: “and else” is not grammatically correct. Appropriate correction is required.
Claim Interpretation
The examiner understands Claims 1, 14 and 16 “and else” to be a grammatical error and interprets the statement to be “or else ”.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-8, 10-11 and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Fukuda et al. (US20190125197, hereinafter “Fukuda”) and in view of Joshi et al (US20200250448, hereinafter “Joshi”)
Claim 1. (Currently Amended) Fukuda teaches A computer-implemented method for face liveness detection, ([0002] “detecting the dynamic state of a living body… detecting a heart rate and so forth of a subject from a temporal change in a face image of the subject taken using a camera”) the method comprising: obtaining one or more color image data frames, each color image data frame depicting a face of a subject; ([0077] “the camera 100 is a visible light color camera, and generates an image signal containing three RGB wavelength components…. camera that can take light reflected from an object (for example, human's face),”) identifying a plurality of skin regions by one of: a) identifying at least one skin region ([0124] “a skin color region 502 (shaded portion) is present in the facial portion of the person.”) in each of a plurality of color image data frames; ([0116] “the sub-regions 501 are multiple regions into which the frame image 500 is divided and which are drawn by broken lines.” And fig. 14-17 and [0081] “each pixel outputted from the frame image analysis unit 220 for one or more frames (for example, 3 frames).” The skin color regions are the subregions which are detected to be part of the human skin ) b) identifying a plurality of skin regions in a single color image data frame; ([0116] “The region division unit 235 divides a frame image to be processed into multiple sub-regions 501” The skin color regions are the subregions which are detected to be part of the human skin) c) identifying a plurality of skin regions in each of a plurality of color image data frames; ([0081] “each pixel outputted from the frame image analysis unit 220 for one or more frames (for example, 3 frames).” In each of these frames, the plurality of skin regions are identified as previously described being skin color regions 502 as shown in fig. 14 and 15)
extracting a skin region data set from each one of the plurality of identified skin regions; ([0084] “the skin color wavelength difference detection unit 240 receives the skin color level signal 203, skin color wavelength data signal 204, and the delay skin color wavelength data signal 206a of each pixel.”)
computing a plurality of color distributions, each color distribution being computed on the basis of one of the plurality of skin region data sets; ([0084] “For pixels in the skin color region, which are identified by the skin color level signal 203 for each frame image, the skin color wavelength difference detection unit 240 acquires an average value of the differences between the skin color wavelength data signal 204 and the delay skin color wavelength data signal 206a, as the skin color wavelength difference data signal 207.” )
determining at least one distance between the plurality of color distributions; ([0085] “the skin color wavelength difference data signal 207 acquired in this modification example can be regarded as a time series change of the average value of the skin color wavelength data signal 204 of the pixels in the skin color region.” Is understood to be the same as the claimed distance in light of instant specifications pg12 para2 )
Fukuda does not explicitly teach if the at least one distance is greater than a liveness threshold, detecting positive liveness of the subject, and else detecting negative liveness of the subject; and outputting the detected positive or negative liveness.
Joshi teaches if the at least one distance ([0022] “a difference between the absorption of the second wavelength range by live human tissue, and the absorption of the first wavelength range by live human tissue satisfies a threshold condition” the wavelength difference is understood to be the same as the claimed distance in light of instant specifications pg12 para2) is greater than a liveness threshold, detecting positive liveness of the subject, ([0022] “the threshold condition can be deemed to be satisfied if a variation between the absorption of the second wavelength range by live human tissue, and the absorption of the first wavelength range by live human tissue is more than about 40%.”) and else (the examiner interprets this to be a grammatical error and interprets this to mean “or else”) detecting negative liveness of the subject; ([0022] “If the determined difference is less than a target value (which may be determined, for example, empirically or via a machine learning process trained on representative training data), the two images can be determined as substantially similar to one another, which is not expected for images of live human tissues. Accordingly, the subject of the images can be determined as a spoof” is understood to be the same as the claimed negative liveness in light of instant specifications page 1) and outputting ([0028] “the authentication engine 315 can also reside on a remote server (e.g., the same server one which the image analysis engine 310 resides, or on a different server) and communicate the result of an authentication process to the kiosk 100.” Is understood to be the same as the claimed outputting in light of instant specifications pg14) the detected positive or negative liveness. ([0036] “a user-interface of the secure system is disposed as a part of a kiosk, identifying a captured image to be a spoof can prevent an access attempt made through the kiosk. On the other hand, if a determination is made that the subject in the image is a live person, an authentication process for determining if the live person is authorized to access the secure system is initiated.”)
It would have been obvious to persons of ordinary skill in the art before the effective filing date of the claimed invention to modify Fukuda to have if a difference between color wavelengths is larger than a threshold then detecting the person is real versus a spoof and outputting the result as taught by Joshi to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been so that (Joshi et al [0019] “allows for improving the discrimination capabilities of biometric authentication systems, thereby improving reliability/security of the systems.”)
Claim 2. (Currently Amended) Fukuda and Joshi teach The method of claim 1,
Fukuda teaches wherein the plurality of skin regions comprise a first skin region (412) and a second skin region, the first skin region being different from the second skin region. (Fig. 16 and Fig. 17 show a first and second skin sub-regions 501 which are different from each other)
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Claim 3. (Currently Amended) Fukuda and Joshi teach The method of claim 2,
Fukuda teaches wherein the first skin region is above an eye level of the subject, and the second skin region is below the eye level of the subject. (Fig. 17 shows that the first skin region is above eye level of the subject and the second skin region is below eye level of the subject)
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Claim 4. (Currently Amended) Fukuda and Joshi teach The method of claim 2, further comprising:
Fukuda teaches extracting a first skin region data set from the first skin region ([0084] “the skin color wavelength difference detection unit 240 receives the skin color level signal 203, skin color wavelength data signal 204, and the delay skin color wavelength data signal 206a of each pixel.”) of a single color image data frame; ([0116] “divides a frame image to be processed into multiple sub-regions 501”)
extracting a second skin region data set from the second skin region of the single color image data frame; (The same process that is done with the first skin region in Fukuda [0084] is also done to the second skin region) and computing a first skin region color distribution on the basis of the first skin region data set; ([0084] “the skin color wavelength difference detection unit 240 receives the skin color level signal 203, skin color wavelength data signal 204, and the delay skin color wavelength data signal 206a of each pixel.”) computing a second skin region color distribution on the basis of the second skin region data set; (The computation of each color wavelength data of each pixel as taught in Fukuda [0084] is understood to be the same as computing the color distribution for the first and second skin regions ) wherein determining the at least one distance comprises determining a distance between the first skin region color distribution and the second skin region color distribution. ([0085] “the skin color wavelength difference data signal 207 acquired in this modification example can be regarded as a time series change of the average value of the skin color wavelength data signal 204 of the pixels in the skin color region.” Is understood to be the same as the claimed distance between the first and second skin region color distribution in light of instant specifications pg12 para2. Fig. 16 shows that phase differencing between each of the skin regions is conducted)
Claim 5. (Currently Amended) Fukuda and Joshi teach The method of claim 4,
Fukuda teaches wherein the plurality of skin regions comprises a third skin region, (Fig. 16 and 17 show there is a third skin region, each box is a skin region) and the method further comprises: extracting a third skin region data set from the third skin region of the single color image data frame; and computing a third skin region color distribution on the basis of the third skin region data set; wherein determining the at least one distance comprises determining at least one distance between the third skin region color distribution and at least one of the first skin region color distribution and the second skin region color distribution. (The operations between the third skin region and the first skin region are the same as the operations done between the first and the second skin regions in claim 4 which is cited in Fukuda [0084] and it is completed for each pixel in the skin color regions)
Claim 6. (Currently Amended) Fukuda and Joshi teach The method of claim 1,
Fukuda teaches wherein the one or more color image data frames ([0077] “the camera 100 is a visible light color camera, and generates an image signal containing three RGB wavelength components…. camera that can take light reflected from an object (for example, human's face),”) comprise only one color image data frame. ([0081] “each pixel outputted from the frame image analysis unit 220 for one or more frames (for example, 3 frames).” )
Claim 7. (Currently Amended) Fukuda and Joshi teach The method of claim 1,
Fukuda teaches wherein the one or more color image data frames ([0077] “the camera 100 is a visible light color camera, and generates an image signal containing three RGB wavelength components…. camera that can take light reflected from an object (for example, human's face),”) comprise a plurality of color image data frames. ([0081] “each pixel outputted from the frame image analysis unit 220 for one or more frames (for example, 3 frames).” )
Claim 8. (Currently Amended) Fukuda and Joshi teach The method of claim 7,
Fukuda teaches wherein the plurality of color image data frames comprise a first color image data frame and a second color image data frame, ([0081] “each pixel outputted from the frame image analysis unit 220 for one or more frames (for example, 3 frames).”) and wherein the method further comprises: extracting a first color image data set from a skin region identified in the first color image data frame; extracting a second color image data set from the skin region([0084] “the skin color wavelength difference detection unit 240 receives the skin color level signal 203, skin color wavelength data signal 204, and the delay skin color wavelength data signal 206a of each pixel.” The operation is completed on all pixels which is understood to be the same as the claimed first color image data set and second color image data set in the first and second color image data frames) computing a first color image color distribution on the basis of the first color image data set; and computing a second color image color distribution on the basis of the second color image data set; ([0084] “For pixels in the skin color region, which are identified by the skin color level signal 203 for each frame image, the skin color wavelength difference detection unit 240 acquires an average value of the differences between the skin color wavelength data signal 204 and the delay skin color wavelength data signal 206a, as the skin color wavelength difference data signal 207.” )
wherein determining the at least one distance comprises determining a distance between the first color image color distribution and the second color image color distribution. ([0085] “the skin color wavelength difference data signal 207 acquired in this modification example can be regarded as a time series change of the average value of the skin color wavelength data signal 204 of the pixels in the skin color region.” Is understood to be the same as the claimed distance between the first color image color distribution and the second color image color distribution in light of instant specifications pg12 para2. This operation is completed for each frame as stated in Fukuda [0105] “difference calculator 247b is a difference between the skin color area signal 245 for a concerned frame and the skin color area signal 245 (that is, the delay skin color area signal 252 read from the area data storage 250) for a frame preceding (for example, immediately preceding) the concerned frame.”)
Claim 10. (Currently Amended) Fukuda and Joshi teach The method of claim 1,further comprising:
Fukuda teaches extracting the one or more color image data frames from video data depicting the face of the subject. ([0033] “detect the change of the subject's skin color over time (time series change), a digital image camera capable of taking a motion image of, for example, about 30 frames per second is used as the camera 100. A subject described herein is a person (human), but may be any animal having a part with less body hair (ex. face)”)
Claim 11. (Currently Amended) Fukuda and Joshi teach The method of claim 1, further comprising:
Fukuda teaches acquiring a plurality of consecutive color image data frame; and averaging the plurality of consecutive color image data frames to obtain the one or more color image data frames. ([0105] “the average wavelength difference calculator 247b is a difference between the skin color area signal 245 for a concerned frame and the skin color area signal 245 (that is, the delay skin color area signal 252 read from the area data storage 250) for a frame preceding (for example, immediately preceding) the concerned frame.”)
Claim 13. (Currently Amended) Fukuda and Joshi teach The method of claim1,
Fukuda teaches wherein the plurality of skin regions comprises a first skin region and a second skin region, (Fig. 16 and Fig. 17 show a first and second skin sub-regions 501 which are different from each other) and wherein the one or more color image data frames comprise a first color image data frame and a second color image data frame, ([0081] “each pixel outputted from the frame image analysis unit 220 for one or more frames (for example, 3 frames).”) and wherein the method further comprises:
extracting a first color image first skin region data set from the first skin region identified in the first color image data frame; extracting a first color image second skin region data set from the second skin region identified in the first color image data frame; extracting a second color image first skin region data set from the first skin region identified in the second color image data frame; ([0084] “the skin color wavelength difference detection unit 240 receives the skin color level signal 203, skin color wavelength data signal 204, and the delay skin color wavelength data signal 206a of each pixel.” The operation is completed on all pixels within all frames which is understood to be the same as the claimed first color image data set and second color image data set in the first and second color image data frames)
computing a first color image first skin region color distribution on the basis of the first color image first skin region data set;
computing a first color image second skin region color distribution on the basis of the first color image second skin region data set;
and computing a second color image first skin region color distribution on the basis of the second color image first skin region data set; ([0084] “For pixels in the skin color region, which are identified by the skin color level signal 203 for each frame image, the skin color wavelength difference detection unit 240 acquires an average value of the differences between the skin color wavelength data signal 204 and the delay skin color wavelength data signal 206a, as the skin color wavelength difference data signal 207.” )
wherein determining the at least one distance comprises determining a distance between the first color image first skin region color distribution and the first color image second skin region color distribution,
and determining a distance between the first color image first skin region color distribution and the second color image first skin region color distribution. ([0085] “the skin color wavelength difference data signal 207 acquired in this modification example can be regarded as a time series change of the average value of the skin color wavelength data signal 204 of the pixels in the skin color region.” Is understood to be the same as the claimed distance between the first color image color distribution and the second color image color distribution in light of instant specifications pg12 para2. This operation is completed for each frame as stated in Fukuda [0105] “difference calculator 247b is a difference between the skin color area signal 245 for a concerned frame and the skin color area signal 245 (that is, the delay skin color area signal 252 read from the area data storage 250) for a frame preceding (for example, immediately preceding) the concerned frame.”)
Claim 14. (Currently Amended) Fukuda teaches An apparatus comprising at least one processor, at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform ([0040] “can be achieved by allowing the processor to execute a predetermined program stored in the storage device.”): obtaining one or more color image data frames, each color image data frame depicting a face of a subject; ([0077] “the camera 100 is a visible light color camera, and generates an image signal containing three RGB wavelength components…. camera that can take light reflected from an object (for example, human's face),”)identifying a plurality of skin regions by one of: a) identifying at least one skin region ([0124] “a skin color region 502 (shaded portion) is present in the facial portion of the person.”) in each of a plurality of color image data frames; ([0116] “the sub-regions 501 are multiple regions into which the frame image 500 is divided and which are drawn by broken lines.” And fig. 14-17 and [0081] “each pixel outputted from the frame image analysis unit 220 for one or more frames (for example, 3 frames).” The skin color regions are the subregions which are detected to be part of the human skin ) b) identifying a plurality of skin regions in a single color image data frame; ([0116] “The region division unit 235 divides a frame image to be processed into multiple sub-regions 501” The skin color regions are the subregions which are detected to be part of the human skin) c) identifying a plurality of skin regions in each of a plurality of color image data frames; ([0081] “each pixel outputted from the frame image analysis unit 220 for one or more frames (for example, 3 frames).” In each of these frames, the plurality of skin regions are identified as previously described being skin color regions 502 as shown in fig. 14 and 15) extracting a skin region data set from each one of the plurality of identified skin regions; ([0084] “the skin color wavelength difference detection unit 240 receives the skin color level signal 203, skin color wavelength data signal 204, and the delay skin color wavelength data signal 206a of each pixel.”)computing a plurality of color distributions, each color distribution being computed on the basis of one of the plurality of skin region data sets; ([0084] “For pixels in the skin color region, which are identified by the skin color level signal 203 for each frame image, the skin color wavelength difference detection unit 240 acquires an average value of the differences between the skin color wavelength data signal 204 and the delay skin color wavelength data signal 206a, as the skin color wavelength difference data signal 207.” )
determining at least one distance between the plurality of color distributions; ([0085] “the skin color wavelength difference data signal 207 acquired in this modification example can be regarded as a time series change of the average value of the skin color wavelength data signal 204 of the pixels in the skin color region.” Is understood to be the same as the claimed distance in light of instant specifications pg12 para2 )
Fukuda does not explicitly teach if the at least one distance is greater than a liveness threshold, detecting positive liveness of the subject, and else detecting negative liveness of the subject; and outputting the detected positive or negative liveness.
Joshi teaches if the at least one distance([0022] “a difference between the absorption of the second wavelength range by live human tissue, and the absorption of the first wavelength range by live human tissue satisfies a threshold condition” the wavelength difference is understood to be the same as the claimed distance in light of instant specifications pg12 para2) is greater than a liveness threshold, detecting positive liveness of the subject, ([0022] “the threshold condition can be deemed to be satisfied if a variation between the absorption of the second wavelength range by live human tissue, and the absorption of the first wavelength range by live human tissue is more than about 40%.”) and else (the examiner interprets this to be a grammatical error and interprets this to mean “or else”) detecting negative liveness of the subject; ([0022] “If the determined difference is less than a target value (which may be determined, for example, empirically or via a machine learning process trained on representative training data), the two images can be determined as substantially similar to one another, which is not expected for images of live human tissues. Accordingly, the subject of the images can be determined as a spoof” is understood to be the same as the claimed negative liveness in light of instant specifications page 1) and outputting ([0028] “the authentication engine 315 can also reside on a remote server (e.g., the same server one which the image analysis engine 310 resides, or on a different server) and communicate the result of an authentication process to the kiosk 100.” Is understood to be the same as the claimed outputting in light of instant specifications pg14) the detected positive or negative liveness. ([0036] “a user-interface of the secure system is disposed as a part of a kiosk, identifying a captured image to be a spoof can prevent an access attempt made through the kiosk. On the other hand, if a determination is made that the subject in the image is a live person, an authentication process for determining if the live person is authorized to access the secure system is initiated.”)
It would have been obvious to persons of ordinary skill in the art before the effective filing date of the claimed invention to modify Fukuda to have if a difference between color wavelengths is larger than a threshold then detecting the person is real versus a spoof and outputting the result as taught by Joshi to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been so that (Joshi et al [0019] “allows for improving the discrimination capabilities of biometric authentication systems, thereby improving reliability/security of the systems.”)
Claim 15. (Currently Amended) Fukuda and Joshi teach The apparatus of claim 14,
Fukuda teaches further comprising a camera configured to measure ([0077] “the camera 100 is a visible light color camera, and generates an image signal containing three RGB wavelength components…. camera that can take light reflected from an object (for example, human's face),”) the one or more color image data frames, ([0081] “each pixel outputted from the frame image analysis unit 220 for one or more frames (for example, 3 frames).”)
Fukuda does not explicitly teach and an interface configured to output the detected positive or negative liveness.
Joshi teaches and an interface configured to output ([0028] “the authentication engine 315 can also reside on a remote server (e.g., the same server one which the image analysis engine 310 resides, or on a different server) and communicate the result of an authentication process to the kiosk 100.” Is understood to be the same as the claimed outputting in light of instant specifications pg14) the detected positive or negative liveness. ([0036] “a user-interface of the secure system is disposed as a part of a kiosk, identifying a captured image to be a spoof can prevent an access attempt made through the kiosk. On the other hand, if a determination is made that the subject in the image is a live person, an authentication process for determining if the live person is authorized to access the secure system is initiated.”)
It would have been obvious to persons of ordinary skill in the art before the effective filing date of the claimed invention to modify Fukuda to have detecting the person is real versus a spoof and outputting the result as taught by Joshi to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been so that (Joshi et al [0019] “allows for improving the discrimination capabilities of biometric authentication systems, thereby improving reliability/security of the systems.”)
Claim 16. (Currently Amended) The non-transitory computer readable medium herein has been executed and performed by the system of claim 14 and is likewise rejected.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Fukuda et al. (US20190125197, hereinafter “Fukuda”) and in view of Joshi et al (US20200250448, hereinafter “Joshi”) and in view of Lee et al (US20120229687, hereinafter “Lee”)
Claim 9. (Currently Amended) Fukuda and Joshi teach The method of claim 8,
Fukuda and Joshi do not explicitly teach wherein a capture time of the second color image data frame is within 0.3 to 0.5 seconds of a capture time of the first color image data frame.
Lee teaches wherein a capture time of the second color image data frame is within 0.3 to 0.5 seconds of a capture time of the first color image data frame. ([0065] “the continuous shooting speed of FIG. 4A is 2.94 frames per second (fps)” is the same as the claimed capture time is within 0.3 and 0.5 seconds which is the same as between 2 fps and 3.33 fps. )
It would have been obvious to persons of ordinary skill in the art before the effective filing date of the claimed invention to modify the proposed combination of Fukuda and Joshi to have a capture time between 0.3 and 0.5 seconds as taught by Lee to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been (Lee et al [0048] “for improving image quality”)
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Fukuda et al. (US20190125197, hereinafter “Fukuda”) and in view of Joshi et al (US20200250448, hereinafter “Joshi”) and in view of Wang et al (US20200019818, hereinafter “Wang”)
Claim 12. (Currently Amended) Fukuda and Joshi teach The method of claim 1,
Fukuda and Joshi do not explicitly teach wherein the at least one distance is selected from the group comprising: Kullback-Leibler divergence, mean shift, Jeffreys divergence, Kolmogorov-Smirnov distance, and earth mover's distance.
Wang teaches wherein the at least one distance is selected from the group comprising: Kullback-Leibler divergence, ([0009] “the distance is the Kullback-Leibler divergence, and the Kullback-Leibler divergence is approximated based on the strong law of large numbers.”)
mean shift, Jeffreys divergence, Kolmogorov-Smirnov distance, and earth mover's distance.
It would have been obvious to persons of ordinary skill in the art before the effective filing date of the claimed invention to modify the proposed combination of Fukuda and Joshi to have a distance calculation method be Kullback-Leibler divergence as taught by Wang to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been because (Wang et al [0027] “good distance metrics are important to many computer vision tasks, such as image classification and content-based image retrieval.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Hanna et al US20110007949 teaches comparing the face image captured with the face image stored previously to authenticate system access
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/OWAIS I MEMON/Examiner, Art Unit 2663
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