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 .
Claim Objections
Claim 10 is objected to because of the following informalities: There are two claims labeled as claim 10. Appropriate correction is required. For Examination purposes the Examiner will consider them as claim 10A and claim 10B.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 6-7, and 10-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lee et al. US 2020/0349372 (hereinafter “Lee”, cited in the IDS).
Regarding claim 12, Lee discloses a method for determining liveness of a target person (see paragraph 0002 "method and apparatus with liveness detection and facial verification") comprising
acquiring a series of frames (paragraph 0078 " liveness detection may be performed using captured images” and 0089" input image may be obtained by an image acquisition apparatus such as a video camera. " ) ;
detecting a face of a target person in each of the frames of the series (see Fig.3 and paragraph 0089, "310 Detects face region in input image " ) –
determining at least one quality feature from each frame of the series (see Fig.3 "320 Measure characteristic information of the face region", paragraphs 0091-0093 “The characteristic information may be a value indicating one or more image features of the face region and/or one or more face object or image-related features in the face region. measure the characteristic information, such as, as a non-limiting example, a hue of the face region, a face tilt indicating a degree of a tilt of the face included in the face region, and a white balance, a brightness, and a gamma of the face region, and the like...") ; -
accepting each frame of the series based on a comparison between at least one predefined capture condition and at least a first quality feature among the at least one quality feature of the frame extracted by the frame quality (see Fig.3 "330, yes, no", paragraph 0094 " In operation 330, the facial verification apparatus determines whether the measured characteristic information satisfies a condition" –
applying at least one first model to a detected face of a target person in a frame, if said frame is accepted by the quality filtering module, to determine a first score based on the detected face of the target person (figure 3 “330 yes” paragraph 0100 performs the liveness detection based on the face region detected in operation 310, wherein 0017 may include using a neural network-based liveness detection model" paragraphs 0101-0102, “The liveness detection model may be, for example, a neural network may provide a liveness value indicating a value, a probability value, or a feature value that indicates whether a face object, which is a test object, is a genuine face or a fake face based on input data. The liveness detection model may be based on a deep convolutional neural network (DCNN) model, as a non-limiting example, in an example, the facial verification apparatus determines a first liveness value based on a first image corresponding to the detected face region. " ; -
applying a second model to the at least one quality feature extracted from a frame, if said frame is accepted by the quality filtering module, to determine a second score based on at least one second quality feature among the at least one quality feature of the frame extracted by the frame quality module (see paragraphs 0103-0104 " determines a second liveness value based on a second image corresponding to a partial region of the detected face region The second liveness value is obtained when image information of the second image is input to a second liveness detection model, The second image may include texture information of the partial face region " ;
applying a fusion model to the first score and the second score to attribute a final score representative of liveness of the target person (paragraphs 0108-0110 " determines whether the test object is live based on the first liveness value, the second liveness value. For another example, the facial verification apparatus may apply a weight to at least one of the first liveness value, the second liveness value and determine the final liveness value based on a result of the applying, for example, a weighted sum."
Claim 1 is similarly analyzed to claim 12.
Regarding claim 2 Lee discloses wherein the first scoring module comprises a first convolutional neuronal network arranged to determine at least one spatial feature of the detected face received as input, and wherein the first score is determined based on the determined spatial feature (see paragraphs 0017, 0101 and 0103-0104).
Regarding claim 6, Lee discloses wherein the at least one predefined capture condition comprises an image sharpness threshold, a gray scale density threshold, a face visibility threshold and/or a light exposure threshold (see paragraphs 0091-0093 a face tilt is equivalent to a face visibility).
Regarding claim 7, Lee discloses the second scoring module comprises a classifier arranged to determine the second score based on second quality features comprising a natural skin color, a face posture, eyes contact, frame border detection and/or light reflection (see paragraph 0102).
Regarding claim 10A, Lee discloses the quality filtering module is arranged to control the frame capture module based on the comparison between the at least one predefined capture condition and the at least one first quality feature of the frame extracted by the frame quality module (see figure 3 “340”).
Regarding claim 10B, Lee discloses the first score is determined for each frame and the second score is determined for each frame, and wherein the final score is determined based on the first scores and the second scores determined for the frames of the series accepted by the quality filtering module (see figure 3 and paragraphs 0100-0103 as discussed above).
Regarding claim 11, Lee discloses wherein the first score is determined based on the detected face of the target person in the frames of the series accepted by the quality filtering module, wherein the second score is determined based on the second quality features of the frames of the series accepted by the quality filtering module (see figure 3 and paragraphs 0100-0103 as discussed above).
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 8-9, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Lee.
Regarding claim 8, as discussed above Lee discloses the limitations of claim 1.
Lee does not explicitly disclose a network to communicate with the first and second scoring modules, however it is well known to carry out processing over a network to which the Examiner declares official notice. The motivation would be to carry out larger processing operations over a network as opposed to an edge device which may not have enough processing power.
Regarding claim 9, Lee discloses that the quality filtering module provides feedback information (see Fig.3 "330" and paragraph 0094 discloses as determining whether the satisfaction of a condition is met or not is the providing of a feedback information.
Claim 14 defines a mere selection of the models used, which is a normal measure to be expected from a skilled person to implement the machine learning models of Lee.
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Mortazavian et al US 2020/0134148 (hereinafter “Mortazavian”, cited in the IDS).
Regarding claim 3, as discussed above, Lee discloses the limitations of claim 2.
Lee does not explicitly disclose wherein the at least one spatial feature comprises a depth map of the detected face received as input, and wherein the first score is determined based on the determined depth map.
Mortazavian discloses using temporal 3D structure of a face to calculate a score and therefore a depthmap information in the context of multifactor liveness detection (see paragraphs 0002, 0009, 0012, and 0070).
Lee and Mortazavian are analogous art because they are from the same field of endeavor of liveness detection.
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine Lee and Mortazavian to use a depth map as an input as taught by Mortazavian. The motivation would be to consider the 3D dimensions of a person’s face in determining the liveliness.
Regarding claim 4, Lee discloses wherein the at least one spatial feature further comprises a light reflection feature and/or a skin texture feature, and wherein the first score is further determined based on the light reflection feature and/or the skin texture feature (see paragraphs 0102 and 0104).
Regarding claim 5, Mortazavian discloses determining at least one temporal feature of the detected face in at least two consecutive frames, and wherein the first score is determined based on the determined spatial feature and based on the determined temporal feature (see paragraphs 0002, 0009, 0012, and 0070).
Allowable Subject Matter
Claim 13 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached 892 notice of references cited.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN B STREGE whose telephone number is (571)272-7457. The examiner can normally be reached M-F 9-5 (PST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached at (571)272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN B STREGE/Primary Examiner, Art Unit 2669
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