Abstract: The present teaching relates to method, system, medium, and implementations for biometric-based authentication. When an input image representing biometric information of a person to be authenticated is received, a feature vector of the input image is generated based on the biometric information captured in the input image via a convolution neural network (CNN) that is realized using one or more linear activation functions. The person is authenticated based on the feature vector in accordance with a biometric-based recognition model.

Abstract: The present teaching relates to method, system, medium, and implementations for biometric authentication. Rotation covariant convolution kernels at multiple convolution layers are obtained with weights learned via machine learning based on rotation invariant (RI) training data. For an input image with fingerprint information captured therein related to a person to be authenticated, an initial feature map is obtained and then at each convolution layer, a feature map is processed based on the RC convolution kernels for the layer to output a rotation covariant (RC) feature map, with the feature map being either the initial feature map and an output RC feature map from a previous convolution layer. The last convolution layer outputs a rotation invariant (RI) feature vector representing fingerprint features of the person in a rotation invariant manner, which is then used to authenticate the person.

Abstract: Embodiments herein disclose a mobile access control system (1000) and method. The system (1000) comprises a mobile device (200), an access control device (300) and a credential management server (100). The mobile device (200) sends an access credential request message for the user to a credential management server (100), receives the credential data and a wireless communication range based on the user from the server (100), verifies the user based on a match between the credential data received from the server (100) and the credential data of the user stored in the mobile device (200), activates an access control application in the mobile device (200), and encrypts the received data. The access control device (300) establishes a connection with the mobile device (200) and receives the encrypted data from the mobile device (200) using the short-range wireless communication, decrypts the encrypted data, and controls the access to the secured area.

Abstract: The present teaching relates to method, system, medium, and implementations for detecting liveness. When an image is received with visual information claimed to represent a palm of a person, a region of interests (ROI) in the image that corresponds to the palm is identified. Each of a plurality of fake palm detectors individually generates an individual decision on whether the ROI corresponds to a specific type of fake palm that the fake palm detector is to detect. Such individual decisions from the plurality of fake palm detectors are combined to derive a liveness detection decision with respect to the ROI.

Abstract: The present teaching relates to detecting palm liveness. When 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. When the initial dimension is smaller than a specified dimension, the initial ROI is extended in some respective directions to some expansion region with certain expansion dimension to generate an ROI using the visual information in the ROI from the image. A plurality of decisions are obtained with respect to the ROI, each of which is made individually on whether the ROI represents a specific type of fake palm. The decisions are then combined to derive a liveness detection decision on whether the palm captured in the image is live.

Abstract: The present teaching relates to method, system, medium, and implementations for detecting liveness. An input image is received with visual information claimed to represent a palm of a person. One or more variations of the input image are generated based on one or more models specifying conditions to generate the variations. The liveness of the visual information is detected based on the input image and the one or more variations thereof.