The present invention relates to a method for classifying a biometric trait represented by an input image, the method being characterized in that it comprises the implementation, by data processing means (21) of a client (2), of the steps of: (a) Determining, for each of a predefined set of possible general patterns of biometric traits, by means of a convolutional neural network, CNN, whether said biometric trait presents or not said general pattern.

The present disclosure relates to secure storage of a detailed set of elements relating to fingerprint features for a finger and to a method for authenticating a candidate fingerprint of a finger using said detailed set of elements, allowing for improved security and user convenience.

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.

A fingerprint verification method includes selecting one or more first fingerprint groups from among a plurality of fingerprint groups based on an input fingerprint image, each fingerprint group of the plurality of fingerprint groups including partial fingerprint images; and determining whether verification is successful based on the input fingerprint image and each of the partial fingerprint images included in the one or more first fingerprint groups.

The disclosure relates to an enrolment method carried out by a smart card using an internal or external electrical power supply, comprising: determining an applied mode among a first and second mode in which the electrical power supply is respectively below and above a predefined threshold; obtaining of N fingerprints in the form of image pixels, N being an integer greater than or equal to 2; extracting digital data representative of characteristic points of the fingerprints by a reading of the pixels at a predetermined resolution level; and generating a fingerprint template by aggregation of the digital data, the enrolment further comprising: determining based on the applied mode of at least one among the number N and the resolution level, so that the fingerprint template has a higher definition in the second mode than in the first mode.

An apparatus, method and system are provided for sensing an individual's biometric information, and generating and transmitting an acoustic signal representative of the sensed biometric information. The acoustic signal may be transmitted as an audio signal or an ultrasonic signal to another apparatus in the system for authentication or verification of the individual's identity.

A method for biometric identification or authentication is described. An image of a body region is obtained. A truth map for said body region is obtained, said truth map associating, with each portion of a set of portions of said image of a body region, a probability that said portion belongs to a true body region. The image of the body region is then compared with a group of reference biometric data using the truth map. Finally, the identification or authentication of said body region is validated or invalidated in response to said comparison.

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.

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.

Methods, systems, and programming for user identification are presented. In one example, a system for acquiring biometric information is disclosed. The system comprises a housing including a surface for a person to place a finger thereon. The system also comprises a sensor, a first image acquisition portion, and a second image acquisition portion. The sensor is configured for sensing presence of the finger when the person places the finger on the surface. The first image acquisition portion is configured for acquiring a fingerprint image of the finger placed on the surface. The second image acquisition portion is configured for acquiring a finger vein image of the finger placed on the surface. The first and second image acquisition portions acquire their respective images at different times.