DETAILED ACTION
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 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.
Claim(s) 1-5, 10-14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 20230245502 A1) in view of Larson (US-20200366671-A1).
Regarding Claim 1, representative of Claims 10 and 19, Li teaches a palm image processing method performed by a computer device, the method comprising:
obtaining a palm image ([abstract]: an image is received with visual information claimed to represent a palm of a person, an initial region of interests (ROI) is identified from the image that corresponds to the palm and an initial dimension thereof is determined), and performing feature extraction on the palm image, to obtain image features at a plurality of scales ([0057]: the ROI identifier 620 may be provided to identify more than one ROIs or at different resolution levels on each image, [0056]: Local features are extracted from ROIs of palms identified from the actual and fake palm images. Global features are extracted);
fusing the image features at the plurality of scales, to obtain an image fusion feature ([0057]: Global features and the grouped local features are fused);
determining, based on the image fusion feature, a frame identifying a palm part in the palm image as a palm part image ([0057]: global features and the grouped local features are fused, and the fused features are used for classification by the detector 520-1. Examiner interpreting the identification as a palm part image to be a classification of whether the palm ROI is part of a live image);
Li does not explicitly teach performing image encryption on the palm part image to obtain encrypted palm part image data; and
transmitting the encrypted palm part image data to a server, wherein the server is configured for performing identity recognition based on the encrypted palm part image data
Larson teaches performing image encryption on the palm part image to obtain encrypted palm part image data ([0015]: biometric data (e.g., facial data, hand/palm data, voice data, etc.) is/are collected, [0266]: use of strong encryption and signing protects authentication (e.g., biometric and/or identity) data when it is stored and transmitted); and
transmitting the encrypted palm part image data to a server, wherein the server is configured for performing identity recognition based on the encrypted palm part image data ([0044]: the one or more of the IVS servers 145 may implement geometric object recognition algorithm(s), wherein features are identified by analyzing…extracted landmarks/features, such as…palmar skin patterns (e.g., lines, creases, mounts (or bumps) on the palm of a human hand)), [0160] In most embodiments, the palm/hand comparison will be performed automatically by the IVS 140 to confirm the match, [0060]: in one example implementation, communications may take place over a network…between one device…and nodes in the IVS cloud… a suitable point-to-point encryption (P2PE) or end-to-end encryption (E2EE) mechanism may be used, which involves endpoint applications handling the encryption and decryption).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have modified Li to include the teachings of Larson by including an authentication step after detecting whether or not a palm print is live or fake. Doing so would improve the accuracy of authentication by ensuring the palm of a real human is being used in the authentication step.
Regarding Claim 2, representative of Claim 11, the Li and Larson combination teaches the method according to claim 1. In addition, Li teaches wherein the performing feature extraction on the palm image, to obtain image features at a plurality of scales comprises:
performing a slicing operation on the palm image through a backbone network, to obtain slice images at the plurality of scales ([0060]: feature pyramid network (FPN)… a feature extractor that takes a single scale image of any size as an input and outputs proportionally sized feature maps at multiple levels, in a fully convolutional fashion, [0063] To extract local features from each of such ROI images, FPN may be again applied to the ROI images to extract local features); and
respectively performing feature extraction on the slice images at the plurality of scales through the backbone network, to obtain the image features at the plurality of scales ([0063] To extract local features from each of such ROI images, FPN may be again applied to the ROI images to extract local features. Examiner notes a feature pyramid network is known in the art for having an architecture that generates feature maps at different scales).
Regarding Claim 3, representative of Claim 12, the Li and Larson combination teaches the method according to claim 2. In addition, Li teaches wherein the performing a slicing operation on the palm image through the backbone network, to obtain slice images at the plurality of scales comprises:
determining, through the backbone network and based on the palm image, a slice image at a maximum scale in the plurality of scales ([0060]: feature extractor that takes a single scale image of any size as an input and outputs proportionally sized feature maps at multiple levels, in a fully convolutional fashion, see element 710 of Fig. 7A, although referring to a feature extraction involved in selecting the ROI, [0063] states FPN may be again applied to the ROI images to extract local features. Element 710 depicts starting from the largest feature map and down sampling); and
using, through the backbone network, the slice image at the maximum scale as a first layer, and downsampling layer by layer, to obtain the slice images at the plurality of scales including the slice image at the first layer (see element 710 of Fig. 7A, although referring to a feature extraction involved in selecting the ROI, [0063] states to extract local features from each of such ROI images, FPN may be again applied to the ROI images to extract local features. Element 710 depicts starting from the largest feature map and down sampling).
Regarding Claim 4, representative of Claim 13, the Li and Larson combination teaches the method according to claim 2. In addition, Li teaches wherein the performing a slicing operation on the palm image through the backbone network, to obtain slice images at the plurality of scales comprises:
downsampling and splicing, through the backbone network, pixels in the palm image at the plurality of scales, to obtain the slice images at the plurality of scales, wherein there are two adjacent pixels in slice images at different scales, and a quantity of pixels between the two pixels at which sampling occurs in the palm image is different ([0060]: feature extractor that takes a single scale image of any size as an input and outputs proportionally sized feature maps at multiple levels, in a fully convolutional fashion, see element 710 of Fig. 7A, although referring to a feature extraction involved in selecting the ROI, [0063] states to extract local features from each of such ROI images, FPN may be again applied to the ROI images to extract local features. Examiner notes the resultant feature maps would be at different scales and thereby adjacent pixels would contain a different quantity of pixels between them).
Regarding Claim 5, representative of Claim 14, Li and Larson combination teaches the method according to claim 1. In addition, Li teaches wherein the fusing the image features at the plurality of scales, to obtain an image fusion feature comprises:
inputting the image features at the plurality of scales into a neck network for feature fusion, to obtain the image fusion feature ([0056]: the machine learning engine 600 comprises … a feature fusion unit 660, [0060] FIG. 7A depicts an exemplary high-level system architecture of an exemplary palm liveness detection mechanism 700 implemented with a multilayer artificial neural network, see Fig. 7A feature fusion as part of the network).
Allowable Subject Matter
Claims 6-9, 15-18, and 20 are 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JANICE VAZ whose telephone number is (703)756-4685. The examiner can normally be reached Monday-Friday 9:00-5:00pm.
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/JANICE E. VAZ/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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