Technologies are presented herein in support of a system and method for performing fingerprint recognition. Embodiments of the present invention concern a system and method for capturing a user's biometric features and generating an identifier characterizing the user's biometric features using a mobile device such as a smartphone. The biometric identifier is generated using imagery captured of a plurality of fingers of a user for the purposes of authenticating/identifying the user according to the captured biometrics and determining the user's liveness. The present disclosure also describes additional techniques for preventing erroneous authentication caused by spoofing. In some examples, the anti-spoofing techniques may include capturing one or more images of a user's fingers and analyzing the captured images for indications of liveness.

A computer-implemented method for generating a representation for a fingerprint includes receiving, by a computer processor, an image of a given fingerprint. The method extracts particular attributes for the given fingerprint from the image using a first neural network. The first neural network is trained to identify particular attributes in fingerprints and constructs a first feature vector from the extracted particular attributes, where the first feature vector has a first fixed length. The method includes extracting textural features of the given fingerprint from the image using a second neural network, where the second neural network is trained to identify textural features that are not limited to particular attributes and constructing a second feature vector from the extracted textural features, where the second feature vector has a second fixed length. The method includes concatenating the first feature vector with the second feature vector to form a representation for the given fingerprint.

The present disclosure provides a fingerprint identification method and apparatus, a storage medium, and a terminal. The fingerprint identification method includes: collecting a to-be-identified fingerprint image of a to-be-identified fingerprint; calculating an image correlation between the to-be-identified fingerprint image and a standard fingerprint image, wherein the standard fingerprint image is a fingerprint image collected from a standard fingerprint; and determining whether the to-be-identified fingerprint is consistent with the standard fingerprint according to the image correlation. The solution provided by the present disclosure can effectively improve the accuracy of fingerprint identification.

Provided is a biometric recognition apparatus and a fingerprint feature extraction method that can automatically optimize parameters used for extracting a feature template from a biometric image. The biometric recognition apparatus includes: a teacher data generation unit that generates a genuine pair and an imposter pair of a first biometric image and a second biometric image; a learning data generation unit that uses a plurality of different temporary parameters to extract feature templates from the first biometric image and the second biometric image; and an optimum solution determination unit that calculates a score separation degree on the temporary parameter basis based on a first score representing a similarity degree of a pair of the feature templates extracted from the genuine pair and a second score representing a similarity degree of a pair of the feature templates extracted from the imposter pair and determines the temporary parameter based on a level of the score separation degree.

A fingerprint inputting method used in an electronic device that includes a display screen and an under-screen fingerprint module. The method includes: displaying a setting interface of fingerprint input having a button of adding fingerprint function; in response to detecting an instruction on the fingerprint adding function, obtaining at least one screen image by the under-screen fingerprint module while a user finger is not pressing a fingerprint identification region; displaying a guiding interface of fingerprint input on the display screen and obtaining a plurality of fingerprint images while the user finger is pressing the fingerprint identification region by the under--screen fingerprint module; and generating a fingerprint model according to the at least one screen image and the plurality of fingerprint images.

A biometric recognition apparatus and fingerprint feature extraction method can automatically optimize parameters used for extracting a feature template from a biometric image. The biometric recognition apparatus includes: a teacher data generation unit that generates a genuine pair and an imposter pair of first and second biometric images; a learning data generation unit that uses a plurality of different temporary parameters to extract feature templates from the first biometric image and the second biometric image; and an optimum solution determination unit that calculates a score separation degree on the temporary parameter basis based on a first score representing a similarity degree of a pair of the feature templates extracted from the genuine pair and a second score representing a similarity degree of a pair of the feature templates extracted from the imposter pair and determines the temporary parameter based on a level of the score separation degree.

Provided are an information processing apparatus, an information processing method, and a storage medium capable of acquiring feature information relating to sweat gland pores that can realize highly accurate identification of an individual. The information processing apparatus includes: a sweat gland pore extraction unit that extracts sweat gland pores from an image including a skin marking; and an information acquisition unit that acquires sweat gland pore information including position information about the sweat gland pore and directional information about the sweat gland pore for each of the sweat gland pores.

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.

The present disclosure provides a fingerprint identification method and apparatus, a storage medium, and a terminal. The fingerprint identification method includes: collecting a to-be-identified fingerprint image of a to-be-identified fingerprint; identifying whether the to-be-identified fingerprint belongs to a first type of fingerprint; calculating an image correlation between the to-be-identified fingerprint image and a standard fingerprint image, when it's determined that the to-be-identified fingerprint belongs to the first type of fingerprint; calculating a feature point matching result between the to-be-identified fingerprint image and the standard fingerprint image when it's determined that the to-be-identified fingerprint does not belong to the first type of fingerprint; and determining whether the to-be-identified fingerprint is consistent with the standard fingerprint; wherein the standard fingerprint image is a fingerprint image collected from a standard fingerprint. The solution provided by the present disclosure can effectively improve the accuracy of fingerprint identification.

A developed fingerprint level determination apparatus includes: a fingerprint image acquisition unit configured to obtain at least two or more developed fingerprint images according to a fingerprint development technique; and a ridge level determination unit configured to check respective ridge development level information of the obtained fingerprint images and compare the respective ridge development level information with each other.