Provided are an information processing apparatus, an information processing method, and a storage medium capable of drawing a fine ridge line with high accuracy. The information processing apparatus includes: an information acquisition unit that acquires sweat gland pore information including position information about a sweat gland pore extracted from an image including a skin marking and directional information about the sweat gland pore for each of the sweat gland pores; and a ridge line drawing unit that draws a quasi ridge line based on the sweat gland pore information.

An apparatus for estimating bio-information according to an embodiment of the present disclosure includes a first sensor configured to obtain a fingerprint image of a finger of the user; a second sensor configured to measure a force or a pressure applied by the finger; and a processor configured to obtain, based on the fingerprint image, a first feature value related to pulse waves and a second feature value related to skin elasticity; obtain a third feature value based on the force or the pressure; and estimate the bio-information of the user based on the first feature value, the second feature value, and the third feature value.

Techniques for classifying records in a dataset are described. The method includes identifying a set of features of a target record and assigning a scoring metric to each feature in the set of features. The method also includes processing the set of features based on the scoring metric to obtain a reduced subset of features and an expanded subset of features. The method also includes searching a store of electronic records using the reduced subset of features to obtain a reduced subset of electronic records. The method also includes searching the reduced subset of electronic records using the expanded subset of features to obtain a set of matching electronic records.

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.

A method for extracting a signature of a fingerprint shown on a source image is described. For this purpose, the source image is transformed into the frequency domain. One or more ridge frequencies are next determined by means of a convolutional neural network applied to said transformed image, n being an integer greater than or equal to 1. The source image is normalized in response to said n ridge frequencies determined. One or more signatures of said fingerprint are finally extracted from said n normalized images.

The authentication information processing method includes acquiring related information generated for each of a plurality of base points representing feature points of biometric information extracted from an image, for a collation base point and a registration base point. The collation base point is included in authentication information for collation used for biometric authentication. The registration base point is included in authentication information for registration. The related information associates attribute information with a central base point. The method includes determining whether the attribute information associated with the collation base point matches the attribute information associated with the registration base point, and comparing the collation base point and the registration base point for which it is determined that the respective attribute information match each other, and determining a correspondence between the authentication information for collation and the authentication information for registration used to calculate a degree of similarity.

A fingerprint authentication method and device, display device, and biometric security apparatus include: a fingerprint sensor; a minutiae extraction circuit connected to the fingerprint sensor; a minutiae storage circuit connected to the minutiae extraction circuit; and a minutiae match circuit connected to the minutiae extraction circuit and the minutiae storage circuit, wherein the minutiae match circuit includes a class-wise comparator having a first input connected to the minutiae extraction circuit for receiving sensed fingerprint minutiae from each class of a plurality of classes, a second input connected to the minutiae storage circuit for receiving stored fingerprint minutiae belonging to each same class of the plurality of classes, respectively, and an output providing a fingerprint authentication signal responsive to the class-wise comparator.

An electronic apparatus and an operating method thereof are provided. The electronic apparatus includes an optical fingerprint sensor and a processor. The optical fingerprint sensor is configured to obtain a fingerprint image. The processor is coupled to the optical fingerprint sensor and analyzes the fingerprint image to calculate at least one of a color pixel ratio and a light reflectance corresponding to the fingerprint image. The processor searches a background database according to at least one of the color pixel ratio and the light reflectance to obtain a background image. The processor corrects the fingerprint image according to the background image to obtain a corrected fingerprint image.

Biometrics fingerprint matching has been done with a heavily hand-tuned and designed process of classical computer vision techniques for several decades. This approach has led to accurate solutions for solving crimes today and, as such, little effort has been devoted to using deep learning in this domain. Exemplary embodiments disclosed herein leverage synthetic data generators to train a neural fingerprint enhancer to improve matching accuracy on real fingerprint images.

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.