A minutia features generation apparatus comprises: an input part to input an image formed as a curved stripe pattern by a ridge line(s); a generation part to generate a skeleton image formed by extracting a skeleton(s) from the image; an extraction part to extract a plurality of minutiae from the skeleton image; and a calculation part configured to calculate a relation minutia feature(s) representing relationship between a first minutia and a second minutia among the plurality of minutiae, wherein the calculation part calculates as one of the relation minutia features defined by a crossing count of the skeleton(s) and a straight line connecting from the second minutia to a nearest neighbor point which is a point on a trace line traced by tracing starting from the first minutia, which point is located at a shortest line distance from the second minutia.

A vehicle includes a first sensor including a capacitance sensor; a second sensor including an ultrasonic sensor; a storage configured to store ultrasonic pattern images; and a controller electrically connected to the first sensor, the second sensor and the storage and configured to: wake up the second sensor based on a user contiguous to the first sensor, obtain a fingerprint pattern image from the second sensor, according to a difference between an area value of a ridge area of the fingerprint pattern image and an area value of a valley area adjacent to the ridge area being less than a predetermined reference value, obtain result data by assigning a weight to the area value of the valley area, and according to the result data and the ultrasonic pattern image data matching more than a predetermined matching value by comparing the result data and ultrasonic pattern image data, recognize as a fingerprint corresponding to the user.

A surface texture recognition sensor, a surface texture recognition device and a surface texture recognition method thereof, and a display device are disclosed. The surface texture recognition sensor is configured to recognize a ridge and a valley of a surface, and includes: a first dielectric layer and a second dielectric layer which overlap with each other; a light source which is configured to emit light into the first dielectric layer; and a photosensitive detector which is at a side of the second dielectric layer away from the first dielectric layer. The light emitted from the light source is incident onto the interface with an incident angle; with the recognition unit being in contact with the surface, refractive index of at least one of the first dielectric layer and the second dielectric layer is changed to allow a critical angle of total reflection to be changed.

The present disclosure relates to methods and devices for fingerprint recognition. In an aspect, a method of a fingerprint sensing system of extracting at least one fingerprint descriptor from an image captured by a fingerprint sensor for enrolment in the fingerprint sensing system is provided. The method comprises capturing images of a finger contacting the fingerprint sensor, detecting at least one fingerprint feature in each captured image, extracting fingerprint data from an area surrounding a location of the detected feature in each captured image, and extracting a fingerprint descriptor by performing a transform of the fingerprint data extracted from the area surrounding the location of the detected feature in each captured image, wherein a size of the area is selected such that the area comprises sufficient information to allow a duplicated descriptor to be discarded for the captured images.

A method for detection of altered fingerprints includes receiving, by at least one processor, an image of a fingerprint from a fingerprint reader. The image has an image resolution. The processor determines a spatial location of the fingerprint within the image. The processor crops the image around the spatial location to provide a cropped image. The processor generates multiple derived images using the cropped image, such that each derived image has the image resolution. The processor generates a multiple-channel image using the derived images. The processor scales the multiple-channel image to an image size. The processor generates a score using a machine learning model. The score is based on the multiple-channel image and is indicative of a likelihood that the fingerprint has been altered.

A striped pattern image examination support apparatus includes a feature extraction part, a central line collation part, and a display part. The feature extraction part extracts, from each of a first striped pattern image and a second striped pattern image, at least central lines and feature points, as a feature of each of the first striped pattern image and the second striped pattern image. The central line collation part performs collation of the respective central lines of the first striped pattern image and the second striped pattern image, and computes corresponding central lines between the first striped pattern image and the second striped pattern image. The display part determines a display form of each of the central lines based on the computed corresponding central lines and superimposes and displays the central lines on each of the first striped pattern image and the second striped pattern image, according to the determined display form.

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.

In a method for determining whether a finger is a real finger at an ultrasonic fingerprint sensor, a first image of a fingerprint pattern is captured at an ultrasonic fingerprint sensor, wherein the first image is based on ultrasonic signals corresponding to a first time of flight range. A second image of the fingerprint pattern is captured at the ultrasonic fingerprint sensor, wherein the second image is based on ultrasonic signals corresponding to a second time of flight range, the second time of flight range being delayed compared to the first time of flight range. A difference in a width of ridges of the fingerprint pattern in the first image compared to the width of ridges of the fingerprint pattern in the second image is quantified. Based on the quantification of the difference, a probability whether the finger is a real finger is determined.

The present invention relates to a method for extracting features of interest from a fingerprint represented by an input image, the method being characterized in that it comprises the implementation, by data processing means (21) of a client equipment (2), of steps of: (a) Estimation of at least one candidate angular deviation of an orientation of said input image with respect to a reference orientation, by means of a convolutional neural network, CNN; (b) Recalibration of said input image as a function of said estimated candidate angular deviation, so that the orientation of the recalibrated image matches said reference orientation; (c) Processing said recalibrated image so as to extract said features of interest from the fingerprint represented by said input image.

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.