Biometric identification device with ultrasonic transducer for fingerprint and microvascular detection

Abstract

Ultrasound transducer biometric identification device for fingerprint and microvascular detection. This biometric identification device comprises an ultrasonic transducer circuit, a control circuit, and a data processing circuit, configured to perform the following: - acquisition of a surface image of an item placed against a capture surface, and a series of volume images of the item from which an overall volume image (114) of a microvascular structure of the item is determined; - determination of features of a fingerprint of the item from the surface image, and of features of the microvascular structure of the item from the overall volume image (114); - comparison of the previously determined features with expected theoretical features to identify the item. Figure for the abstract: Fig. 5

Description

Title of the invention: Biometric identification device with ultrasonic transducer for fingerprint and microvascular detection technical field

[0001] The present description relates to the field of biometric identification devices based on ultrasonic transduction capture and detection. Prior art

[0002] A biometric identification device makes it possible to verify or determine the identity of a user of the device based on a measurement of one or more biometric characteristics of the user such as a fingerprint, facial shape, iris pattern, retinal pattern, etc. The different techniques for measuring one or another of these characteristics each have advantages and disadvantages according to different criteria including: more or less significant error rate, ease or difficulty of stealing the template with which the measurements are compared, possibility of detecting an imitation, proof or not of life, ease and comfort of use, size of the sensor required, energy consumption required.

[0003] It is known to produce a biometric identification, anti-spoofing, and liveness detection device using a fingerprint sensor that performs optical or capacitive measurements to verify whether the captured fingerprint corresponds to the expected one (identity verification phase), and then to implement a liveness detection and anti-spoofing algorithm via image processing performed on the captured fingerprint or by using an infrared system, for example, to detect whether the finger placed on the device during fingerprint capture is a fake finger. Summary of the invention

[0004] There is a need to propose a biometric identification device that does not present at least some of the disadvantages of existing solutions.

[0005] An embodiment overcomes all or part of the drawbacks of known solutions and proposes a biometric identification device comprising at least one ultrasonic transducer circuit, a control circuit and a data processing circuit, configured to implement at least the following steps:

[0006] - acquisition of at least one surface image of at least one element intended to be arranged against a capture surface of the device, and at least one series of volume images of the element from which an overall volume image of a micro- vasculature of the element is determined;

[0007] - determination of characteristics of an imprint of the element from the image surface, and microvasculature characteristics of the element from the overall volume image;

[0008] - comparison of the previously determined characteristics with characteristics expected theoretical risks to identify or not the element.

[0009] According to a particular embodiment, the data processing circuit is configured to determine the overall volume image by implementing a spatio-temporal filtering process of the series of volume images of the element.

[0010] According to a particular embodiment, the ultrasonic transducer circuit is configured to acquire the series of volume images of the element by pulsed Doppler imaging.

[0011] According to a particular embodiment, the data processing circuit is configured to implement, between the step of acquiring the surface image and the series of volume images and the step of determining the characteristics of the footprint and microvascularity of the element, a step of aligning the surface image and the overall volume image with respect to each other.

[0012] According to a particular embodiment, the data processing circuit is configured such that the step of determining the characteristics of the footprint and microvascular structure of the element comprises at least:

[0013] - determination of minutiae of the imprint of the element from the image of surface, and points of interest of the microvasculature of the element from the overall volume image;

[0014] - calculation of the position of at least one minutiae in the surface image and / or of less one point of interest in the overall volume image, and geometric relationships between said at least one minutiae and / or said at least one point of interest and at least some of the other minutiae and points of interest taking into account, for the points of interest, the depths at which the points of interest are located in the overall volume image;

[0015] and such that the comparison step includes a comparison of the previously calculated position and geometric relationships with an expected theoretical position and geometric relationships to identify or not the element.

[0016] According to a particular embodiment, the points of interest correspond to bifurcations of micro-blood vessels and / or to points of strong curvature of micro-blood vessels.

[0017] According to a particular embodiment, the data processing circuit is configured to implement, between the minutiae and interest point determination step and the calculation step, a combination step, in the form of a point cloud, minutiae and determined points of interest.

[0018] According to a particular embodiment, the data processing circuit is configured such that, during the calculation step, the other minutiae and points of interest with which the geometric relations are calculated correspond to the minutiae included in a region of the surface image delimited by a planar geometric figure and to the points of interest included in a region of the global volume image included in a volume formed from the planar geometric figure which is extended in a dimension of the global volume image perpendicular to the surface image.

[0019] According to a particular embodiment, the plane geometric figure corresponds to a polygon, a circle or an ellipse.

[0020] According to a particular embodiment, the data processing circuit is configured such that the calculation step is repeated for each minutia and each point of interest determined, and that the comparison step is carried out for the position and geometric relationships calculated for each minutia and each point of interest.

[0021] According to a particular embodiment, the data processing circuit is configured such that the geometric relationships calculated between a first minutia or point of interest and a second minutia or point of interest include the distance between the first minutia or point of interest and the second minutia or point of interest, an orientation of the first minutia or point of interest with respect to the second minutia or point of interest, and an orientation of the second minutia or point of interest with respect to the first minutia or point of interest.

[0022] According to a particular embodiment, the data processing circuit is configured such that, for a point of interest corresponding to a bifurcation or a point of strong curvature of micro-blood vessels, the calculated geometric relationships include the orientations of each of the micro-blood vessels with respect to the other minutiae and points of interest.

[0023] According to a particular embodiment, the data processing circuit is configured as follows:

[0024] - the determination of the characteristics of the imprint and microvasculature of the element involves the extraction of at least one feature vector from the surface image and the overall volume image by implementing a deep learning algorithm;

[0025] - the comparison of the previously determined characteristics with characteristics expected theoretical risks to identify or not the element involves a comparison, by the deep learning algorithm, of the previously determined feature vector with at least one expected theoretical feature vector.

[0026] According to a particular embodiment, the data processing circuit is configured such that the comparison step includes a score calculation whose value depends on the correlations between the determined characteristics and the expected theoretical characteristics to identify or not the element.

[0027] According to a particular embodiment, the data processing circuit is configured such that, during the comparison step, a score is calculated for each minutiae and each point of interest determined. Brief description of the drawings

[0028] These features and advantages, as well as others, will be described in detail in the following description of particular embodiments, given by way of non-limiting example, in relation to the accompanying figures, among which:

[0029] - Figures [Fig. 1] and [Fig. 2] schematically represent an identification device biometric according to a particular embodiment;

[0030] - Figure 3 represents, in the form of a schematic diagram, the steps involved implemented by a biometric identification device according to a specific embodiment;

[0031] - Figure 4 schematically illustrates minutiae identified in an image of surface captured by a biometric identification device according to a particular embodiment;

[0032] - [Fig. 5] schematically represents a region delimiting the minutiae and the points of interest considered during an identification carried out by a biometric identification device according to a particular embodiment;

[0033] - Figure 6 schematically represents an example of calculation results obtained when the identity of the user of the biometric identification device is confirmed;

[0034] - Figure 7 schematically represents a first example of calculation results obtained when the identity of the user of the biometric identification device is not confirmed;

[0035] - Figure 8 schematically represents a second example of calculation results obtained when the identity of the user of the biometric identification device is not confirmed. Description of the embodiments

[0036] The same elements have been designated by the same reference numerals in the different figures. In particular, the structural and / or functional elements common to the different embodiments may have the same reference numerals and may have identical structural, dimensional and material properties.

[0037] For the sake of clarity, only the steps and elements useful for understanding the described embodiments have been shown and are detailed. In particular, various elements (ultrasonic transducer circuit, control circuit, processing circuit, ...) and the various steps involved (processing of acquired images, determination of characteristics, details of calculations performed, etc.) are not detailed. A person skilled in the art will be able to perform these elements in detail based on the functional description provided here.

[0038] Unless otherwise specified, when referring to two elements connected together, this means directly connected without intermediate elements other than conductors, and when referring to two elements coupled together, this means that these two elements can be connected or linked through one or more other elements.

[0039] In the following description, when reference is made to absolute position qualifiers, such as the terms "front", "back", "top", "bottom", "left", "right", etc., or relative position qualifiers, such as the terms "above", "below", "superior", "inferior", etc., or to orientation qualifiers, such as the terms "horizontal", "vertical", etc., reference is made, unless otherwise specified, to the orientation of the figures in a normal position of use.

[0040] Unless otherwise specified, the expressions "approximately", "roughly", and "in the order of" mean within 10%, preferably within 5%.

[0041] A biometric identification device 100 according to a particular embodiment is described below in relation to figures 1 and 2.

[0042] In the example shown in [Fig. 1], the device 100 is configured to perform biometric identification based on an element 101 of the user of the device 100, this element 101 corresponding, for example, to one or more fingers of this user. The device 100 includes a capture surface 102 on which this element 101 of the user of the device 100 is intended to be placed during identification.

[0043] The device 100 comprises at least one ultrasonic transducer circuit 104, a control circuit 106, and a data processing circuit 108. In the schematic example shown in [Fig. 2], the control circuit 106 can be electrically coupled to the ultrasonic transducer circuit 104 and thus be capable of transmitting signals to the ultrasonic transducer circuit 104, controlling the emission of ultrasound for the measurements to be performed, and also capable of receiving electrical measurement signals transmitted by the ultrasonic transducer circuit 104. The control circuit 106 can also be coupled to the data processing circuit 108 in order to transmit to the data processing circuit 108 the measurement results obtained from the ultrasonic transducer circuit 104.

[0044] The circuit 104 may include a plurality of ultrasonic transducers enabling the image acquisitions required for biometric identification. The ultrasonic transducers are, for example, arranged in a matrix or other manner adapted to the captures to be made. The number of ultrasonic transducers in circuit 104 may depend on the dimensions of the capture surface 102.

[0045] In the described embodiment, the device 100 can be configured to acquire at least one surface image of the user's finger(s) placed on and in contact with the capture surface 102 of the device 100, i.e., an acquisition of at least one image of at least one fingerprint formed of ridges and valleys present on the surface of the skin of the finger(s) placed on the capture surface 102. In the device 100, the acquisition of a surface image of the user's finger(s) placed on and in contact with the capture surface 102 is based on the fact that the ultrasound emitted by the transducer circuit 104 is reflected more significantly against the air present in the valleys of the fingerprint of the finger(s) than against the ridges of the fingerprint.

[0046] Furthermore, the device 100 can also be configured to acquire volume images of the finger(s) placed on and in contact with the capture surface 102 of the device 100, and from which it is possible to determine, by processing these volume images, an overall volume image of the microvasculature of this or these finger(s), i.e. an image capture of the microvessels of the finger(s) of the user of the device 100. The overall volume image obtained can thus be deduced from several volume images captured successively at non-zero depths (dimension along the Z-axis visible on [Fig. 1] and which is, for example, substantially perpendicular to the capture surface 102) of the finger(s) present on the capture surface 102.

[0047] The ultrasonic transducer circuit 104 can, for example, be configured to acquire volume images of the finger(s) placed on the capture surface 102 using pulsed-wave Doppler imaging. In such a configuration, the transducer circuit 104 is configured to emit a series of ultrasonic pulses. The responses obtained in such a configuration do not correspond to a change in the frequency of the received wave, but rather to a pseudo-Doppler effect, that is, a change in the time interval between the received echoes relative to the time interval between the emitted waves. In the device 100, pulsed-wave Doppler imaging can therefore be used to determine the spatial location of microvascular movements in the acquired volume images, from which a microvascular map is determined in the overall volume image.

[0048] The ultrasonic transducer circuit 104 may, for example, include CMUT type transducers ('Capacitive Micromachined Ultrasonic Transducer') or PMUT type transducers ('Piezoelectric Micromachined Ultrasonic Transducer'). machined piezoelectric) or of another type.

[0049] In the example embodiment described here, the control circuit 106 can be configured to provide the ultrasonic transducer circuit 104 with electrical excitation signals causing the emission of ultrasonic waves by the ultrasonic transducer circuit 104, and to receive electrical response signals generated by the ultrasonic transducer circuit 104 under the effect of receiving ultrasonic waves reflected by the element 101 present on the capture surface 102.

[0050] In the described embodiment, the data processing circuit 108 can be configured to analyze and process the electrical response signals received by the control circuit 106 and sent by the ultrasonic transducer circuit 104. The processing circuit 108 may include, for example, a microprocessor coupled with a memory to perform the processing of the received data.

[0051] The control circuit 106 and the data processing circuit 108 can be configured to perform biometric identification by implementing the steps described below and represented in diagram form in [Fig.3].

[0052] In a first step 202, the device 100 can detect an element present on the capture surface 102. If an element is indeed detected on the capture surface 102, a second step 204 can be implemented. If no element is detected, this detection step 202 can be repeated until an element is detected on the capture surface 102. The detection step 202 can be repeated continuously or at regular or irregular time intervals.

[0053] The device 100 can then be configured to acquire at least one surface image of the element 101 present on the capture surface 102 (step 204). For example, this configuration of the device 100 may include sending an acquisition command, or a sequence of instructions, to the control circuit 106 for acquiring the surface image of the element 101.

[0054] In a subsequent step 206, an acquisition of at least one surface image of the element 101 present on the capture surface 102 can then be implemented. This acquisition may include the emission of a series of ultrasonic signals in the form of pulses by the transducer circuit 104, then the reception of the echoes by the transducer circuit 104 and the processing of the responses obtained to obtain the surface image of the element 101, i.e. the image of the user's fingerprint(s).

[0055] The processing of the responses obtained to acquire the surface image of element 101 corresponds, for example, to signal processing of the "beamforming" or "micro-beamforming" type, also called beamforming or spatial filtering. Alternatively, it is possible to transmit and receive signals from the transducer circuit 104 by multiplexing, to achieve image acquisition in B-mode. focused on the surface of element 101. According to another variant, it is possible to perform orthogonal addressing of the transducer matrix of circuit 104 to emit a series of ultrasonic pulses in the form of focused waves, or plane waves, or to perform full matrix capture of the RCA-OPW type ("Row column addressing orthogonal plane wave imaging"). As an example, the document by V. Perrot et al., "So you think you can DAS? A viewpoint on delay-and-sum beamforming", Ultrasonics 111 (2021), 106309, arXiv:2007.11960, describes an example of a DAS (Delay-and-sum) beamforming method that can be used to obtain the surface image of element 101.

[0056] The acquisition of the surface image of element 101 may also include the implementation of other steps not detailed here: filtering of the response signal obtained, envelope detection, logarithmic compression, etc.

[0057] The acquired surface image may correspond to the capture of an imprint of element 101, for example, one or more fingerprints when element 101 corresponds to one or more fingers of the user. The data relating to the acquired image is, for example, stored in a memory of device 100 or in an external memory of device 100, for example, connected to device 100 by a communication link.

[0058] After acquiring this surface image of the element 101, the device 100 can be configured to perform successive acquisitions over time of several volume images of the element 101 present on the capture surface 102 (step 208). For example, this configuration of the device 100 may include sending an acquisition command, or a sequence of instructions, to the control circuit 106 for acquiring volume images of the element 101.

[0059] In a subsequent step 210, several successive volume images of the element 101 present on the capture surface 102 can be acquired, these volume images can then be processed to highlight displacements or movements related to blood circulation in the microvasculature of the element 101 and ultimately obtain an overall volume image of the microvasculature of the element 101. The processing carried out on the volume images can correspond to the implementation of spatio-temporal filtering, or "clutter filtering", by high-pass filtering in the so-called "slow-time" domain after demodulation of the signals.Alternatively, the estimation of blood flow-related motion in the microvasculature can be performed by correlating motion between successive volume images, or by SVD (singular value decomposition) and rejection of components that include tissue motion and noise. See the document by Demené C et al., “Spatiotemporal Clutter Filtering of Ultrafast Ultrasound Data Highly Increases Doppler and fUltrasound Sensitivity”. IEEE Trans Med Imaging. 2015 Nov, 34(11), 2271-85, describes an example of implementation of such spatio-temporal filtering.

[0060] For example, volume image acquisition can be achieved by means of plane ultrasonic waves emitted by the transducer circuit 104 at a number of angles, then the processing of the response signals can include a beamforming phase followed by coherent addition of the images corresponding to each angle to obtain a B-mode volume image. This process can be repeated over time to obtain several volume images allowing monitoring of blood flow in the microvasculature of element 101 and ultimately obtaining, after processing the volume images, an overall volume image of the microvasculature of element 101. As an example, see the document by G. Montaldo et al., "Coherent plane-wave compounding for very high frame rate ultrasonography and transient elas-tography," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 56, no. 3, pp.489-506, Tuesday 2009, describes an example of a DAS beamforming method with coherent summation of plane waves that can be implemented to obtain the global volume image of the microvasculature of element 101.

[0061] Thus, the microvasculature of element 101 is determined by identifying beforehand, using the series of volume images acquired, the movements of tissues and arterial walls due to blood circulation allowing localization of the elements of the microvasculature.

[0062] The data relating to the overall volume image obtained can be stored in a memory of the device 100 or in an external memory of the device 100 for example connected to the device 100 by a communication link.

[0063] In the particular embodiment described, during a subsequent step 212, the previously acquired surface image and global volume image can be aligned with each other, for example by repositioning them according to a common coordinate system defined by the geometry of the transducer circuit 104.

[0064] In a subsequent step 214, from the previously acquired surface image, the data processing circuit 108 can determine or extract minutiae from the fingerprint of the element 101 obtained from this image. This minutiae determination can be performed by one or more image processing algorithms not described in detail here but known to those skilled in the art. Various methods for extracting minutiae from a fingerprint image that can be used here are described in the paper by R. Bansal et al., “Minutiae Extraction from Fingerprint Images - a Review”, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No. 3, September 2011.

[0065] In a subsequent step 216, from the global volume image pre- Previously acquired, the data processing circuit 108 can determine or extract points of interest from the microvasculature of the finger(s) present on the capture surface 102. In the described embodiment, these microvasculature points of interest correspond to bifurcations of micro-blood vessels and / or points of strong curvature, or strong inflection, of micro-blood vessels. This determination of the points of interest can be performed by one or more image processing algorithms, for example, similar to those implemented for the previous determination of the fingerprint minutiae. Alternatively, the algorithm implemented to determine the points of interest may include the following steps:

[0066] - segmentation of the overall volume image, for example, either by characteristics risks of a Hessian matrix in each voxel of the global volume image, either by using a convolutional neural network or other;

[0067] - image skeletonization (or "thinning") segmented, with the aim of reducing the thickness of elements (e.g., blood vessels) present in the image to a single voxel;

[0068] - detection of bifurcations and / or strong inflections;

[0069] - characterization of the orientation of each point of interest, for example by analysis in principal components or PCA (“Principal component analysis” in English).

[0070] According to another variant, the algorithm implemented to determine the points of interest may include the use of a neural network to directly detect the points of interest as well as their type (bifurcation or strong curvature) and their orientation.

[0071] Alternatively, the surface image acquisition can be performed after the volume images have been acquired. Furthermore, the determination of minutiae in the surface image can also be performed before or during the acquisition of the volume images, or between the acquisition of the volume images and their processing.

[0072] In the particular embodiment described, during a subsequent step 218, a combination step, in the form of a point cloud, is implemented, combining the minutiae and the previously determined points of interest. This combination step may involve placing the impression minutiae and the microvascular points of interest in the same coordinate system linked to the geometry of the transducer circuit 104. Thus, the impression minutiae can be placed in an (X, Y) plane at a shallow depth or zero depth (Z = 0), and the microvascular points of interest can be positioned in a volume located below that in which the minutiae are located. This combination of data from previous image acquisitions is, for example, implemented by the data processing circuit 108.

[0073] The data processing circuit 108 can then perform the calculation of the position of at least one minutia in the surface image and / or of at least one point of interest in the overall volume image, and the calculation of geometric relationships between said at least one minutia and / or said at least one point of interest and at least a part of the other minutiae and points of interest taking into account, for the points of interest, the depths (dimension along the Z axis of [Fig.1]) at which the points of interest are located in the overall volume image.In a particular embodiment, the data processing circuit 108 is configured such that, during this calculation step, the other minutiae and points of interest with which the geometric relations are calculated correspond to the minutiae included in a region of the surface image delimited by a plane geometric figure (for example, a polygon, a circle or an ellipse) and to the points of interest included in a region of the global volume image delimited by the volume resulting from this plane geometric figure and extended in the depth direction (parallel to the Z-axis) of the global volume image.

[0074] An example of such a calculation is described below in relation to figures 4 and 5.

[0075] In [Fig. 4], an example of a surface image acquired by the device 100 is shown. presented and designated by reference 110. This surface image 110 corresponds here to the fingerprint of a finger present on the capture surface 102 of the device 100. The minutiae determined in this surface image 110 are represented by points and designated by references 112, 113.

[0076] An example of a global volume image acquired by the device 100 is shown in [Fig. 5] and designated by reference 114. In [Fig. 5], this image corresponds to the result obtained by maximum intensity projection (or MIP for "Maximum Intensity Projection") along the Z-axis of the microvasculature of the finger present on the capture surface 102 of the device 100. The points of interest determined in this image are represented by points designated by reference 116.

[0077] In the example described here, one of the minutiae or one of the points of interest 116 can then be chosen, and then a region of the surface image 110 and a region of the overall volume image 114 superimposed on each other and including this minutiae or point of interest 116 are delimited, on one side by a planar geometric figure for the surface image 110, and by a volume formed from the planar geometric figure for the overall volume image 114. In the example of Figures 4 and 5, one of the minutiae, which is designated by the reference 113, is chosen and the planar geometric figure is defined as a circle whose center corresponds to the minutiae 113. The calculation step is implemented by considering the other minutiae 112 included in the geometric figure and the points of interest 116 included in a volume 120 formed by projecting this geometric figure along the Z axis, that is to say in the direction of the thickness of element 101. The [Fig.5] schematically represents the volume 120 obtained and delimiting the region of the . or fingers in which the minutiae 112, 113 and the points of interest 116 are considered for this calculation. In the example of [Fig. 5], this volume 120 forms a cylinder extending through at least part of the thickness of the finger(s) present on the capture surface 102.

[0078] Thus, the minutiae 112, 113 and the points of interest 116 are projected into a common 3D space from which the following calculation steps are implemented.

[0079] The position of minutiae 113 in the surface image 110 can be calculated, and geometric relationships between this minutiae 113 and the other minutiae 112 and the points of interest 116 included in the volume 120 can also be calculated. In the example described, these geometric relationships may include:

[0080] - the distance between minutiae 113 and each of the other minutiae 112 and points of interest 116 included in volume 120, and

[0081] - an orientation of the minutiae 113 with respect to the other minutiae 112 and points of interest 116 included in volume 120, and

[0082] - an orientation of the other 112 minutiae and 116 points of interest included in the volume 120 compared to the meticulousness of 113.

[0083] This calculation can be implemented by considering all the minutiae 112 and points of interest 116 present in a volume whose section corresponds to the chosen geometric figure and which extends into at least part of the thickness of the finger or fingers present on the capture surface 102.

[0084] In one particular embodiment, the data processing circuit 108 is configured such that the calculation step described above is repeated for each minutia 112 and each previously determined point of interest 116. When this calculation is implemented for a point of interest 116 corresponding to a bifurcation or a sharp bend in micro-blood vessels, the calculated geometric relationships may include the orientations of each of the micro-blood vessels relative to the other minutiae 112 and points of interest 116.

[0085] For the calculations described above, the positions of the minutiae 112, 113 and the points of interest 116 as well as the geometric relationships between them are calculated in Cartesian or spherical coordinates.

[0086] The implementation of this or these calculation steps corresponds to step 220 of the diagram in [Fig.3].

[0087] The position(s) of minutiae 112, 113 and points of interest 116 and the previously calculated geometric relationships are then compared with one or more positions and geometric relationships previously calculated, for example, during a preliminary user enrollment step, to confirm or not the identity of the user of the device 100 (step 222). This comparison can be performed for the positions and geometric relationships calculated for each minutia 112 and each Point of interest 116.

[0088] Figure 6 schematically illustrates an example in which the calculated positions and geometric relationships correspond to those expected, thus confirming that the identity of the user of the device 100 is indeed the expected one. In this figure, reference numeral 122 designates the expected positions and geometric relationships between minutiae 124 and points of interest 126, which are similar to those calculated for minutiae 112, 113 and points of interest 116 of the finger present on the capture surface 102 of the device 100.

[0089] Figure 7 schematically illustrates another example in which the calculated positions and geometric relationships do not correspond to those expected to confirm the identity of the user of the device 100. In this example, the determined minutiae 112, 113, the positions of these minutiae, and the geometric relationships between them do correspond to those expected, but no point of interest 116 is determined (and therefore no geometric relationship with points of interest is calculated). Such an example corresponds, for instance, to the case where a fake finger is placed on the capture surface 102 of the device 100. In such a case, the device 100 may consider that the identity of the user of the device 100 is not confirmed.

[0090] Figure 8 schematically illustrates another example in which the calculated positions and geometric relationships do not correspond to those expected to confirm the identity of the user of the device 100. In this example, the minutiae 112, 113 and the points of interest 116 determined, their positions, and the geometric relationships between them do not correspond to those expected. Such an example corresponds, for instance, to the case where the finger placed on the capture surface 102 of the device 100 is a real finger but is not that of the person intended to be identified. In such a case, the device 100 may consider that the identity of the user of the device 100 is not confirmed.

[0091] In a particular embodiment, in order to compare the calculations performed with the expected results to confirm or refute the identity of the user of device 100, the comparison step may include a score calculation (step 224 in the diagram of [Fig. 3]) whose value depends on the correlations between, on the one hand, the calculated position and geometric relationships and, on the other hand, the theoretical position and geometric relationships expected to confirm or refute the user's identity. For example, the score calculation method may yield a score close to or equal to 1 in a case such as that described previously in relation to [Fig. 6], and a score close to 0 in cases such as those described previously in relation to Figures 7 and 8.

[0092] According to one example, such a score can be calculated for each minutiae and each point of interest determined previously. Then, it is possible to select a part or the totality of the scores obtained and calculate an overall score representative of the scores previously calculated (sum, average, linear combination, etc.). The scoring method is not described in detail here and may correspond to an adaptation of one of the methods known to those skilled in the art and, for example, applied for identification by fingerprint comparison.

[0093] The score value is then compared with a threshold value in order to assess the correspondence between the biometric measurement performed and the expected biometric data, and thus confirm or not the identity of the user (step 226).

[0094] In the embodiment described above, the features of the footprint of element 101 determined from the surface image 110 correspond to the minutiae 112, 113 of this footprint, and the features of the microvasculature of element 101 determined from the overall volume image correspond to the points of interest 116 of the microvasculature. According to another embodiment, it is possible that the determination of the features of the footprint and the microvasculature of the element involves extracting at least one feature vector from the surface image and the overall volume image by implementing a deep learning algorithm.In this case, comparing the previously determined features with expected theoretical features to identify or reject element 101 may involve a comparison, by the deep learning algorithm, of the previously determined feature vector with at least one expected theoretical feature vector. In this other example, it is also possible to perform a score calculation whose value depends on the correlations between the determined feature vector and the expected one to identify or reject element 101. See the paper by Y. Tang et al., “FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction,” arXiv: 1709.02228, and the paper by Engelsma, Joshua J. et al., “Learning a Fixed-Length Fingerprint Representation.”IEEE Transactions on Pattern Analysis and Machine Intelligence 43 (2019): 1981-1997, describe examples of deep learning algorithms that can be implemented for this other example of realization.

[0095] Device 100 can thus form a complete biometric system integrating identification, anti-spoofing, and liveness detection functionalities, based on a single compact representation of information from the captured surface imprint and also information from the microvasculature. Device 100 can, in particular, enable:

[0096] - a better level of security compared to an identification device based on only on a fingerprint scan;

[0097] - a better level of anti-usurpation guarantee and liveness detection due to the reliability of data relating to microvasculature.

[0098] In the example described above, the device 100 performs biometric identification by comparing the measured data contained in the surface image and the volume image with expected data corresponding to those of a person whose identity is being verified. Alternatively, the device 100 may compare the measured data with expected data corresponding to those of several people, in which case the device 100 can verify whether the user corresponds to one of these people.

[0099] In the description above, the device 100 can compare the measurements taken with expected data stored in the device's memory. Alternatively, the device 100 can connect to a remote database containing expected data corresponding to that of one or more individuals to verify whether the user of the device 100 is that individual or one of those individuals. In another alternative, the device 100 can be connected to a smart card containing data recorded during prior enrollment. The data comparison step can then be performed on the smart card rather than on the device 100.

[0100] In the above description, the device 100 is used to directly confirm or deny the identity of the user of the device 100. Prior to this identification, the device 100 can be used to perform an initial data acquisition (surface image(s) and volume images) during a prior enrollment, which will serve as expected data during future identifications of the user of the device 100. Other uses of the device 100 can also be envisaged.

[0101] Various embodiments and variations have been described. A person skilled in the art will understand that certain features of these various embodiments and variations could be combined, and other variations will become apparent to a person skilled in the art.

[0102] Finally, the practical implementation of the embodiments and variants described is within the reach of a person skilled in the art, based on the functional indications given above.

Claims

Claims

1. Biometric identification device (100) comprising at least one ultrasonic transducer circuit (104), a control circuit (106) and a data processing circuit (108), configured to implement at least the following steps: - acquisition (206, 210) of at least one surface image (110) of at least one element (101) intended to be arranged against a capture surface (102) of the device (100), and of at least one series of volume images of the element (101) from which an overall volume image (114) of a microvasculature of the element (101) is determined; - determination (214, 216, 218, 220) of characteristics of an imprint of the element (101) from the surface image (110), and of characteristics of the microvasculature of the element (101) from the global volume image (114);- comparison (222, 224, 226) of the previously determined characteristics with theoretical characteristics expected to identify or not the element (101).;

2. A biometric identification device (100) according to claim 1, wherein the data processing circuit (108) is configured to determine the overall volume image (114) by implementing a spatio-temporal filtering method of the series of volume images of the element (101).

3. Biometric identification device (100) according to one of the preceding claims, wherein the ultrasonic transducer circuit (104) is configured to carry out the acquisition of the series of volume images of the element (101) by pulsed Doppler type imaging.

4. Biometric identification device (100) according to one of the preceding claims, wherein the data processing circuit (108) is configured to implement, between the step of acquiring (206, 210) the surface image (110) and the series of volume images and the step of determining (214, 216, 218, 220) the characteristics of the imprint and the microvasculature of the element (101), a step of aligning (212) the surface image (110) and the overall volume image (114) with respect to each other.

5. Biometric identification device (100) according to one of the preceding claims, wherein the data processing circuit (108) is configured such that the step of determining (214, 216, 218, 220) the characteristics of the imprint and the microvasculature of the element (101) comprises at least: - determining (214, 216) minutiae (112, 113) of the imprint of the element (101) from the surface image (110), and points of interest (116) of the microvasculature of the element (101) from the overall volume image (114);- calculating (220) the position of at least one minutia (113) in the surface image (110) and / or at least one point of interest (116) in the global volume image (114), and geometric relationships between said at least one minutia (113) and / or said at least one point of interest (116) and at least a portion of the other minutiae (112) and points of interest (116) taking into account, for the points of interest (116), the depths at which the points of interest (116) are located in the global volume image (114); and such that the comparison step (222, 224, 226) comprises a comparison of the position and geometric relationships previously calculated with a theoretical position and geometric relationships expected to identify or not the element (101).;

6. A biometric identification device (100) according to claim 5, wherein the points of interest (116) correspond to bifurcations of micro-blood vessels and / or to points of strong curvature of micro-blood vessels.

7. Biometric identification device (100) according to one of claims 5 or 6, wherein the data processing circuit (108) is configured to implement, between the step of determining (214, 216) the minutiae (112, 113) and the points of interest (116) and the calculation step (220), a step of combining (218), in the form of a point cloud, the minutiae (112, 113) and the points of interest (116) determined.

8. A biometric identification device (100) according to one of claims 5 to 7, wherein the data processing circuit (108) is configured such that, during the calculation step (220), the other minutiae (112) and points of interest (116) with which the geometric relationships are calculated correspond to the minutiae (112) included in a region of the surface image (110) delimited by a planar geometric figure and to the points of interest (116) included in a region of the global volume image (114) included in a volume (120) formed from the planar geometric figure which is extended in a dimension of the global volume image (114) perpendicular to the surface image (110).

9. A biometric identification device (100) according to claim 8, wherein the planar geometric figure (118) corresponds to a polygon, a circle or an ellipse.

10. A biometric identification device (100) according to one of claims 8 or 9, the data processing circuit (108) is configured such that the calculation step (220) is repeated for each minutia (112, 113) and each point of interest (116) determined, and the comparison step (222, 224, 226) is performed for the position and geometric relationships calculated for each minutia (112, 113) and each point of interest (116).

11. The biometric identification device (100) of one of claims 5 to 10, wherein the data processing circuit (108) is configured such that the calculated geometric relationships between a first minutiae (113) or point of interest (116) and a second minutiae (112) or point of interest (116) include the distance between the first minutiae (113) or point of interest (116) and the second minutiae (112) or point of interest (116), an orientation of the first minutiae (113) or point of interest (116) relative to the second minutiae (112) or point of interest (116), and an orientation of the second minutiae (112) or point of interest (116) relative to the first minutiae (113) or point of interest (116).

12. A biometric identification device (100) according to one of claims 5 to 11, wherein the data processing circuit (108) is configured such that, for a point of interest (116) corresponding to a bifurcation or a point of high curvature of micro-blood vessels, the calculated geometric relationships include the orientations of each of the micro-blood vessels relative to the other minutiae and points of interest.

13. A biometric identification device (100) according to one of claims 1 to 4, wherein the data processing circuit (108) is configured such that: - the determination (214, 216, 218, 220) of the characteristics of the imprint and of the microvasculature of the element (101) comprises the extraction of at least one vector of characteristics of the surface image (110) and of the global volume image (114) by the implementation of a deep learning algorithm; - the comparison (222, 224, 226) of the previously determined characteristics with theoretical characteristics expected to identify or not the element (101) comprises a comparison, by the deep learning algorithm, of the previously determined vector of characteristics with at least one expected theoretical vector of characteristics.

14. Biometric identification device (100) according to one of the preceding claims, in which the data processing circuit (108) is configured such that the comparison step (222, 224, 226) comprises a calculation (224) of a score whose value depends on the correlations between the determined characteristics and the theoretical characteristics expected to identify or not the element (101).

15. Biometric identification device (100) according to one of claims 5 to 12 and according to claim 14, wherein the data processing circuit (108) is configured such that, during the comparison step (222, 224, 226), a score is calculated for each minutia (112, 113) and each point of interest (116) determined.

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