Abstract: An evaluation method of a biometric identification function implemented from a candidate biometric data set and a reference biometric data set, the biometric identification function, applied to a candidate biometric data item and a reference biometric data item, returning a match, the method comprising steps of: obtaining pairs of false matches, each pair comprising a candidate biometric data item and a reference biometric data item not associated with a same known individual; determining, for each candidate biometric data item in a pair, a first number associated with said item and equal to the number of pairs of false matches obtained comprising said item, and/or a second number associated with said reference biometric data item and equal to the number of pairs of false matches obtained comprising said item; calculating a bias score based on a maximum of the first numbers and/or based on a maximum of the second numbers.

Abstract: A method for generating an image of a palm based on a first partial image of a palm of the individual and on a second partial image of a palm including detecting first characteristic points in the first image and second characteristic points in the second image, generating a plurality of pairs of characteristic points by matching first characteristic points with second characteristic points, computing an overall geometric transformation associated with said matching, determining a local geometric transformation for each pair of points, and merging the first image with the second image as a function of the overall geometric transformation and of the local geometric transformations, in order to generate the palm image.

Abstract: 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.

Abstract: A method of detecting at least one element of interest visible on an input image, by means of a convolutional neural network, CNN, the method comprises the steps of: (a) extracting, by means of an ascending branch of a first subnetwork of said CNN of feature pyramid network, FPN, type, a plurality of initial feature maps (C1, C2, C3, C4, C5) representative of the input image at different scales, said FPN further comprising a descending branch and lateral connections between the ascending branch and the descending branch, at least one lateral connection comprising an attention module; (b) generating, by means of said descending branch of the FPN, a plurality of enriched feature maps (P2, P3, P4, P5) also representative of the input image at different scales, each enriched feature map (P2, P3, P4, P5) incorporating the information from the initial feature maps (C1, C2, C3, C4, C5) of smaller or equal scale; (d) detecting at least one element of interest visible on an input image, by means of a second subnetw

Abstract: A method for detecting at least one biometric trait visible in an input image, by means of a convolutional neural network, the method wherein it comprises the implementation, by data processing means of a client, of steps of: (a) Generating, by means of a feature extraction block of said CNN, a plurality of representation vectors each defining a candidate region of interest of said input image potentially containing a biometric trait, the representation vector of a candidate region of interest comprising at least one position value of the candidate region of interest, at least one size value of the candidate region of interest, an orientation value of the candidate region of interest, and an objectivity score of the candidate region of interest; (b) Selecting, by means of a filtering block of said CNN, at least one region of interest from said candidate regions based on the representation vectors thereof.

Abstract: 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.

Abstract: 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.

Abstract: The present invention relates to a method of segmenting an input image representing at least one biometric trait, by means of a convolutional neural network, CNN, the method being characterized in that it comprises the implementation, by data processing means (21) of a client (2), of steps of (b) generating, by means of a first subnetwork of said CNN, referred to as a specialization block, a confidence mask associated with a ridge orientation map of the at least one biometric trait represented by said input image, referred to as a RFM mask; said RFM mask being applied to at least the input image so as to obtain a focused input image; (c) segmenting, by means of a second subnetwork of said CNN, referred to as a segmentation block, the focused input image.

Abstract: 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.

Abstract: 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.

Abstract: An evaluation method of a biometric identification function implemented from a candidate biometric data set and a reference biometric data set, the biometric identification function, applied to a candidate biometric data item and a reference biometric data item, returning a match, the method comprising steps of: obtaining pairs of false matches, each pair comprising a candidate biometric data item and a reference biometric data item not associated with a same known individual; determining, for each candidate biometric data item in a pair, a first number associated with said item and equal to the number of pairs of false matches obtained comprising said item, and/or a second number associated with said reference biometric data item and equal to the number of pairs of false matches obtained comprising said item; calculating a bias score based on a maximum of the first numbers and/or based on a maximum of the second numbers.

Abstract: A method for detecting at least one biometric trait visible in an input image, by means of a convolutional neural network, the method wherein it comprises the implementation, by data processing means of a client, of steps of: (a) Generating, by means of a feature extraction block of said CNN, a plurality of representation vectors each defining a candidate region of interest of said input image potentially containing a biometric trait, the representation vector of a candidate region of interest comprising at least one position value of the candidate region of interest, at least one size value of the candidate region of interest, an orientation value of the candidate region of interest, and an objectivity score of the candidate region of interest; (b) Selecting, by means of a filtering block of said CNN, at least one region of interest from said candidate regions based on the representation vectors thereof.

Abstract: A method of analyzing a fingerprint, the method comprising the step of acquiring a fingerprint image (20) together with the following steps: performing filtering processing on the fingerprint image to estimate, for each pixel of the fingerprint image, a first frequency of the ridges (21) in the fingerprint, and using the first frequencies associated with the pixels of the fingerprint image to produce a first frequency map (22) of the fingerprint image; subdividing the fingerprint image into a plurality of windows each comprising a plurality of pixels, calculating a Fourier transform for each window in order to estimate a second frequency of the ridges for all of the pixels in said window, and using the second frequencies associated with the pixels of the windows to produce a second frequency map of the fingerprint image; and merging the first frequency map and the second frequency map in order to obtain a map of consolidated frequencies of the fingerprint image.

Abstract: A method of detecting at least one element of interest visible on an input image, by means of a convolutional neural network, CNN, the method comprises the steps of: (a) extracting, by means of an ascending branch of a first subnetwork of said CNN of feature pyramid network, FPN, type, a plurality of initial feature maps (C1, C2, C3, C4, C5) representative of the input image at different scales, said FPN further comprising a descending branch and lateral connections between the ascending branch and the descending branch, at least one lateral connection comprising an attention module; (b) generating, by means of said descending branch of the FPN, a plurality of enriched feature maps (P2, P3, P4, P5) also representative of the input image at different scales, each enriched feature map (P2, P3, P4, P5) incorporating the information from the initial feature maps (C1, C2, C3, C4, C5) of smaller or equal scale; (d) detecting at least one element of interest visible on an input image, by means of a second subnetw

Abstract: 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.

Abstract: The present invention relates to a method of segmenting an input image representing at least one biometric trait, by means of a convolutional neural network, CNN, the method being characterized in that it comprises the implementation, by data processing means (21) of a client (2), of steps of: (b) generating, by means of a first subnetwork of said CNN, referred to as a specialization block, a confidence mask associated with a ridge orientation map of the at least one biometric trait represented by said input image, referred to as a RFM mask; said RFM mask being applied to at least the input image so as to obtain a focused input image; (c) segmenting, by means of a second subnetwork of said CNN, referred to as a segmentation block, the focused input image

Abstract: A method for obtaining an image of at least one fingerprint from a sequence of images is disclosed. The method comprises a segmentation of images in a sequence of acquired images making it possible to determine regions liable to comprise a fingerprint, a tracking of regions determined along with the sequence of images and a calculation of a confidence mark for each region determined. The confidence mark of a region determined is incremented (or respectively decremented) at each acquired image if the region is present in (or respectively absent from) the image, the increment (or respectively the decrement) being dependent on the predefined image acquisition frequency. Information making it possible to obtain a final image containing fingerprints is generated by the method when a predefined number of regions each having reached a confidence mark at least equal to a predetermined threshold is obtained.

Abstract: A method for processing an image representing at least one finger supplied by an image sensor of the thin-film transistor type of a fingerprint-capture system is disclosed. The purpose of the method is to modify characteristics of the image so that they correspond to the characteristics of images acquired by a fingerprint-capture system using a CCD or CMOS sensor while preserving relevant information about the image.

Abstract: 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.

Abstract: The present invention relates to a method of displaying images implemented by a data processing module able to be connected to a display system comprising at least two display zones (ZA1, ZA2), said method of displaying images comprising the following steps: —displaying of a first image (11) in a first display zone (ZA1) of said display system, —displaying of at least one second image (12) obtained by applying to the first image (11) at least one image processing algorithm, each second image (12) being displayed in a second zone (ZA2) of said display system, —as long as one of said second zones (ZA2) displaying one of said second images (12) is hovered over by a pointer, displaying, in all or part of the first display zone (ZA1), of at least one part of said first image to which is applied the at least one processing algorithm making it possible to obtain said second hovered-over image (12).