An information processing apparatus, including: a feature point detecting unit configured to detect a feature point from an image including biological information obtained via a sensor unit; and a feature value extracting unit configured to extract a feature value that characterizes the feature point based on a peripheral image including the feature point.

A fingerprint recognition device is provided. The fingerprint recognition device includes an image acquisition module acquiring a fingerprint image including an input fingerprint, a preprocessing module generating a preprocessed image by preprocessing the fingerprint image, a minutiae extraction module extracting coordinates of each of minutiae and orientation points of the input fingerprint from the preprocessed image and a fake detection module receiving regions-of-interest (ROIs), including the coordinates of each of the minutiae or orientation points of the input fingerprint, and determining whether the input fingerprint is a fake by performing learning using the received ROIs.

Provided is a method for determining a match between a candidate fingerprint and a reference fingerprint characterized by minutiae local features. The method includes evaluating a similarity of the candidate fingerprint local feature and the reference fingerprint local feature of a current local feature pair, and determining a match depending on the similarity evaluation and geometric coherence evaluations performed for said current local feature pair. Other embodiments are disclosed.

Provided is a method for determining a match between a candidate fingerprint and a reference fingerprint characterized by minutiae local features. The method includes extracting several minutiae from the candidate fingerprint, computing from said extracted minutiae a plurality of minutiae local features of the candidate fingerprint, computing a first global matching score between the candidate fingerprint and the reference fingerprint based on the first similarity scores of said matching local feature pairs; computing a second global matching score between the candidate fingerprint and the reference fingerprint based on said computed second similarity scores; and determining a match between the candidate fingerprint and the reference fingerprint comprising: comparing the first and second global matching scores and, comparing the first matching score to a matching threshold. Other embodiments disclosed.

Methods, systems, and programming for identifying a person are presented. In one example, a first set of candidates is determined based on a fingerprint image acquired from a finger of the person. A second set of candidates is determined based on a finger vein image acquired from the finger. The person is recognized based on the first and/or second set of candidates.

Provided is a computer-implemented deep-learning-based method for extracting minutiae from a latent friction ridge image. The method comprises training a minutiae extraction model though a deep-learning network with ground truth latent friction ridge images as training samples, and, inputting a latent friction ridge image into the minutiae extraction model to extract minutiae of the latent friction ridge image, wherein the model outputs locations and directions for the extracted minutiae. The deep-learning network includes a base network configured to generate a minutiae feature map from a latent friction ridge image, a Region Proposal Network (RPN) configured to propose minutiae locations and directions from the minutiae feature map, and a Region-Based Convolutional Neural Network (RCNN) configured to definitely decide minutiae locations and directions from RPN's proposal.

There is provided an information processing apparatus and an information processing method capable of improving security against leakage of a registered fingerprint in fingerprint authentication. Collation is performed on the basis of whether or not a feature amount of a partial region of an entire fingerprint imaged by a fingerprint imaging unit and a method of operation on the fingerprint imaging unit match the feature amount of the partial region of the entire fingerprint and the method of operation registered in advance. Application to a fingerprint authentication device is possible.

Techniques for client side multi-factor password generation include randomly removing one or more features of a record of a fingerprint image of a user and creating a distorted record of the fingerprint image by merging the record with a user input code using an encryption technique, the distorted record being reversible using the user input code. The distorted record for authentication of the user is registered.

Disclosed is a biometric information acquisition system and a processing method thereof. The biometric information acquisition system comprises a transparent substrate; a light source; a photoelectric conversion device, which is located at one side of the transparent substrate with the light source, receives light reflected by the transparent substrate and generates electrical signals; a processing device for obtaining biometric information based on the electrical signals. At least a portion of the transparent substrate is made of flexible material, and fits a predetermined portion of an organism on the other side of the transparent substrate based on a curved interface, therefore, ambient light incident to the photoelectric conversion device is reduced, an interference caused by the ambient light is reduced, accuracy of acquiring biometric characteristic information is improved. Because fitting with the curved interface can increase contact area, more biometric characteristic information of the predetermined portion can be obtained.

A method for controlling unlocking and a terminal includes the follows. Feature points of class i of a finger of a user is acquired by scanning the finger with the i.sup.th scanning partition of a fingerprint recognition sensor of a terminal, the fingerprint recognition sensor has M scanning partitions, M is a positive integer not less than 2 and i is a positive integer not greater than M. The acquired feature points of class i is added into a current feature point set. A matching process is performed between the current feature point set and a pre-stored fingerprint template. The terminal is unlocked when the current feature point set is matched.