A method for protecting a biometric template, comprising the steps of: retrieving an original vector (V) representing said biometric template, said vector comprising a plurality of original elements (v.sub.1, v.sub.2, . . . v.sub.i, . . . , v.sub.n); mapping at least some elements from said original vector to a protected vector (P) comprising a plurality of protected elements (p.sub.1, p.sub.2, . . . p.sub.i, . . . , p.sub.n−m+1), the mapping being based on multivariate polynomials defined by m user-specific coefficients (C) and exponents (E).

The present disclosure provides a method for facilitating implementing biometric recognition. Further, the method may include receiving two or more biometric images of one or more biometric identifiers of one or more individuals from one or more devices. Further, the two or more biometric images may be in two or more spectrums. Further, the method may include analyzing the two or more biometric images using one or more deep hashing network models. Further, the method may include extracting two or more discriminative deep hashing codes from the two or more biometric images based on the analyzing. Further, the method may include generating a biometric template based on the two or more discriminative deep hashing codes. Further, the method may include generating a biometric key for the one or more biometric identifiers using a fuzzy commitment scheme based on the biometric template. Further, the method may include storing the biometric key.

A store management system includes: an acquisition unit that obtains a face image of a customer from an imaging apparatus that captures the face image of the customer at a timing at which the customer intends at least one of to enter a store, to make payment, and to leave the store; a notification unit that notifies the customer that the face image is to be obtained or is obtained by the acquisition unit; and a permission unit that permits the customer at least one of to enter the store, to make payment, and to leave the store on condition that the face image is obtained by the acquisition unit and that the customer is notified by the notification unit. It is thus possible to suitably manage the customer's entrance into the store or the like.

This application discloses a facial recognition method and apparatus, a device and a medium, which relates to the field of image processing. The method includes: fusing a color map and a depth map of a facial image to obtain a fused image of the facial image, the fused image including two -dimensional information and depth information of the facial image (202); dividing the fused image into blocks to obtain at least two image blocks of the fused image (204); irreversibly shuffling pixels in the at least two image blocks to obtain a pixel-confused facial image (206); and determining an object identifier corresponding to the facial image according to the pixel-confused facial image (208).

Conventionally, biometric template protection has been achieved to improve matching performance with high levels of security by use of deep convolution neural network models. However, such attempts have prominent security limitations mapping information of images to binary codes is stored in an unprotected form. Given this model and access to the stolen protected templates, the adversary can exploit the False Accept Rate (FAR) of the system. Secondly, once the server system is compromised all the users need to be re-enrolled again. Unlike conventional systems and approaches, present disclosure provides systems and methods that implement encrypted deep neural network(s) for biometric template protection for enrollment and verification wherein the encrypted deep neural network(s) is utilized for mapping feature vectors to a randomly generated binary code and a deep neural network model learnt is encrypted thus achieving security and privacy for data protection.

An object of the present invention is to provide an image recognition system, an image recognition server, and an image recognition method having a new high security framework that can achieve utilization of multi-device diversity. The image recognition system according to the present disclosure includes a computationally non-intensive encryption algorithm based on random unitary transformation and achieves a high level of security. In addition, the image recognition system achieves high recognition performance by using ensemble learning to integrate recognition results based on the dictionaries of 4 different devices.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for enhancing biometric authentication are disclosed. According to a method, A circumferential biometric template (CBT) of a particular entity is generated based on one or more images of the particular entity. A request to access an item is received wherein the request includes an identifier corresponding to the particular entity. Circumferential biometric data (CBD) for one or more physical characteristics of an entity depicted in an image captured by an image capture device is obtained, Authentication outcome data indicating whether the CBD matches the CBT of the particular entity is generated. Access to the item is granted when the authentication outcome data indicates that the CBD matches the CBT of the particular entity. Access to the item is denied when the authentication outcome data indicates that the CBD fails to match the CBT of the particular entity.

A fingerprint sensing system. The fingerprint sensing system includes: at least one sensor; at least one display device; at least one application processor; and at least one secure enclave processor. The application processor(s) receives fingerprint data from the sensor(s) and provides the fingerprint data to the secure enclave processor(s). The secure enclave processor(s) decodes the fingerprint data and provides a signal indicative of at least one matched node. The application processor(s), responsive to receipt of the signal indicative of the matched node(s), presents at least a portion of a synthetic fingerprint image via at least one display device corresponding to the matched node(s).

A computer alters at least one recognizable metric or text in a digitally encoded photographic image by operating an alteration algorithm in response to user input data while preserving an overall aesthetic quality of the image and obscuring an identity of at least one individual or geographic location appearing in the image. An altered digitally-encoded photographic image prepared by the altering of the at least one recognizable metric or text in the image is stored in a computer memory. User feedback and/or automatic analysis may be performed to define parameter values of the alteration algorithm such that the alteration process achieves preservation of aesthetic qualities while obscuring an identity of interest.

Examples of a system for usage control of a personal data are described. The system may obtain an input image including a first face of a person. Further, the system may compute a usage control matrix based on the input image, at least one usage control function, and a predefined criteria. The pre-defined criteria may be associated with at least one of: a data usage policy, a face matching probability related to matching of the face present in the input image, and a face recognition probability related to a recognition of an identity of the person. Furthermore, by using the input image and the usage control matrix, the system may transform the input image to a usage-controlled image. Furthermore, the system may verify a matching of the face present in the input image with a second face present in the usage-controlled image. Furthermore, the system may recognize an identity of the person in the input image and provide a feedback indicative of a failure to verify the identity of the person from the usage-controlled image.