Biometric encoding method and terminal

Abstract

A method (800, 900, 1000, 11000) for encoding, implemented by an encoding terminal (102, 300), of a biometric template (803, 903, 1003, 1103) for testing (GE-Bio), said method takes, as input data, a biometric characteristic (801, 901, 1001, 11001) for testing (CE-Bio), and provides, as output data, a biometric template (803, 903, 1003, 1103) for testing (GE-Bio), in which the biometric template (803, 903, 1003, 1103) for testing (GE-Bio) is generated from the biometric characteristic (801, 901, 1001, 1101) of test (CE-Bio) according to an encoding scheme (802, 902, 1002, 1102) representative of the distance (802a, 902a, 1002a, 11002a), d(CE-Bio, CR-Bio), according to a metric between the biometric characteristic (801, 901, 1001, 1101) of test (CE-Bio) and a biometric characteristic (802b, 902b, 1002b, 1102b) of reference (CR-Bio).

Description

Title of the invention: Biometric encoding method and terminal technical field

[0001] The present invention relates to a biometric encoding method and terminal. It also relates to an identification method and system implementing the biometric encoding method and terminal. Technical background

[0002] It is common to use identification and / or authentication protocols for individuals based on the comparison of some of their biometric characteristics in order to allow them to access remote services, authorize access to information stored in a collective or personal database, verify an identity or authorize access to a restricted access area.

[0003] Whether during authentication or identification, the comparison of biometric characteristics is generally not performed on the raw data directly from their recording, but on biometric data derived as a result of applying an algorithmic process called encoding. According to section 3.21 of ISO / IEC 19794-1:2011 Information technology — Biometric data interchange formats — Part 1: Framework, the derived biometric data constitutes a "biometric template" or "biometric model" which is distinct from the raw data from which it is derived and can be compared to other biometric templates.

[0004] Biometric authentication generally consists of comparing a biometric proof template acquired for an individual to one or a very limited number of reference biometric templates (1:1). This type of protocol allows a user who wishes to access the resources of an information system, such as an operating system, a network, an application, a service, a database, or an application, to prove their identity using a biometric characteristic. The use of an authentication protocol generally requires a preliminary enrollment step whereby a user identifies themselves by sharing certain information about their identity with the entity implementing the protocol.

[0005] Carry out a remote banking transaction, access a password database stored on a multifunction mobile phone, or verify, at a border crossing or during a check by law enforcement, the identity of an individual carrying an identity document containing a secure electronic element on where biometric information is recorded are common examples of the application of an authentication protocol.

[0006] WO 9526013 Al [MINNESOTA MINING & MFG [US]] 28.09.1995 describes an authentication system that compares a proof biometric characteristic acquired from an individual with a reference biometric characteristic recorded in the system. The system is further configured to detect a variable biometric characteristic to verify the liveness of the individual.

[0007] Unlike authentication, identification requires comparing a test biometric template with numerous other reference biometric templates previously acquired from several individuals (1:N) and generally stored in a database. This type of protocol makes it possible to identify a user from among a set of users. The database of reference biometric templates generally requires a preliminary step of registering biometric templates collected from identified individuals.

[0008] Determining, for example in the context of a police investigation, the identity of a person by comparing a fingerprint of their dermatoglyphics, an image of their iris, or an image of their face with those in a database of known individuals is a common application of an identification protocol. Another example of an application is access to an area restricted to a limited number of individuals.

[0009] US 4109237 A [HILL ROBERT B] 22.08.1978 describes a method for identifying an individual by comparing the pattern of the vein network of his iris with a set of previously recorded vein network patterns of a plurality of individuals.

[0010] It is now common practice for users, when they wish to perform an operation with a remote resource, to authenticate and / or identify themselves using a mobile device, such as a smartphone, tablet, or laptop, connected to that resource. However, biometric data, whether in raw or template form, is highly sensitive personal data. It is necessary to ensure its confidentiality while protecting it from theft and / or identity fraud.

[0011] EP 2 813 961 Al [KONVALINKA IRA [CA]] 17.12.2014 describes a biometric authentication method implementing a mobile device coupled to a remote server. The device includes a biometric sensor and memory on which a personal biometric reference template specific to its user is stored. Upon request from the server, the user acquires a proof biometric characteristic using the mobile device's biometric sensor. The device then generates a proof biometric template and compares it The device uses a personal biometric template and transmits a success or failure signal to the remote server. During the process, the biometric information remains confined to the mobile device and is never shared with the server. Its confidentiality is preserved. However, the remote server has no guarantee as to the actual identity of the mobile device user.

[0012] It is possible to enhance the security of authentication or identification protocols, and in particular to reduce the risk of identity theft, by using a biometric authentication or identification terminal coupled with a mobile device. The terminal is configured to acquire an individual's biometric characteristics and generate an additional biometric proof template. This additional biometric proof template can then be compared to a reference biometric template.

[0013] WO 2017 / 019972 Al [VISA INT SERVICE ASS [US]] 02.02.2017 describes a biometric authentication method implementing a mobile device coupled to an access terminal equipped with a biometric sensor. A personal biometric reference template specific to its user is stored on the mobile device. The mobile device is configured to receive a proof biometric template generated by the access terminal, compare said proof biometric template to the personal biometric reference template, and send the result of the comparison to the access terminal.

[0014] WO 2017 / 075063 Al [VISA INT SERVICE ASS [US]] 04.07.2017 describes a method for authenticating individuals near a biometric access terminal using their mobile devices without requiring the individuals to acquire a biometric characteristic using their mobile device. The access terminal is configured to receive, from each nearby mobile device, a public encryption key generated by applying a first fuzzy extractor function to a personal biometric reference template stored on each mobile device and unique to its user.Next, the access terminal generates a biometric proof template from the biometric characteristics acquired from a user, and generates encryption secrets by applying a second fuzzy extractor function to the biometric proof template and each of the received encryption public keys; there are as many encryption secrets as there are received encryption public keys. Then, it encrypts the biometric proof template with each of the secrets to generate as many biometric templates as there are encryption secrets. These encrypted biometric templates are then sent to all nearby mobile devices. If a mobile device manages to decrypt one of the encrypted biometric proof templates, it compares it to its own personal reference biometric template, and in case of a match... of correspondence, sends a success signal to the access terminal which authorizes the mobile device to access a resource.

[0015] WO 2019 / 078858 Al [VISA INT SERVICE ASS [US]] 25.04.2019 describes a biometric authentication method that limits the risk of a man-in-the-middle attack. A first biometric proof template of an individual is generated by a mobile device such as a smartphone or laptop computer from an initial acquisition of biometric characteristics. This first biometric template is stored locally on the mobile device, and an encrypted copy is sent to an authentication terminal, enabling access to a resource such as a database, a computer network, or a restricted access area.The terminal generates a second biometric template for the individual from a second acquisition of biometric characteristics, calculates an encrypted result from the first encrypted biometric template and the second biometric template by applying an encryption function, and then sends the encrypted result to the mobile device. The mobile device decrypts the encrypted result, compares the first decrypted biometric template with the first locally stored biometric template, and, if they match, compares the first biometric template with the second biometric template. If the first and second biometric templates match, the mobile device sends identification information such as a username, password, or ID number.

[0016] WO 2019 / 094071 Al [VISA INT SERVICE ASS [US]] 16.05.2019 describes a method for biometrically identifying individuals near a biometric access terminal that reduces the number of comparisons between a proof biometric template and reference biometric templates in a database. The terminal has a database of reference biometric templates of previously enrolled users and their mobile devices. The reference biometric templates are stored in an obfuscated manner in the database. When the access terminal detects the mobile device of an enrolled user nearby, it acquires proof biometric characteristics of the mobile device owner, generates a proof biometric template, and compares it to the reference biometric template associated with the mobile device and stored in the database. Summary of the invention

[0017] A major drawback of current authentication or identification methods is the possibility of false acceptance, since an imposter can steal a user's mobile device and submit a biometric characteristic. testing close to the reference biometric characteristic for accessing services provided via the identification or authentication terminal.

[0018] There is therefore a need for a solution that reduces the risk of false acceptance during an identification and authentication process using an intermediary mobile device. Furthermore, such a solution would ideally enforce the confidentiality and security of biometric information.

[0019] According to a first aspect of the invention, an encoding method is provided, implemented by an encoding terminal, for a biometric test template, said method takes, as input data, a biometric test characteristic, and provides, as output data, a biometric test template, in which the biometric test template is generated from the biometric test characteristic according to an encoding scheme representative of the distance according to a metric between the biometric test characteristic and a reference biometric characteristic.

[0020] According to some embodiments, the encoding scheme includes a pre-encoder configured to generate an internal reference biometric template generated from the reference biometric characteristic and an intermediate test biometric template generated from the test biometric characteristic, the distance according to a metric is a distance between the internal reference biometric template and the intermediate test biometric template.

[0021] According to some embodiments, the encoding scheme includes a transition function or a distance-centered distribution function with respect to a metric between an internal reference biometric template generated from the reference biometric characteristic and an intermediate test biometric template generated from the test biometric characteristic.

[0022] According to some embodiments, the encoding scheme further includes a noise generation function, preferably a noise generation function taking, as an input variable, the biometric test characteristic.

[0023] According to some embodiments, the noise generation function (F-Br) comprises a function generating a random number from the biometric test characteristic chosen from a hash function, a weighted summation function or a reduction function.

[0024] According to some embodiments, the encoding scheme is implemented in the form of a neural network previously trained according to a teacher-student protocol.

[0025] According to some embodiments, the encoding scheme is specific to the biometric terminal.

[0026] According to some embodiments, the reference biometric characteristic is specific to the user of the encoding terminal.

[0027] According to a second aspect of the invention, an encoding terminal is provided for the implementation of an encoding method according to any one of the embodiments of the first aspect of the invention.

[0028] According to some embodiments, the encoding terminal is a mobile electronic device, preferably a multifunction mobile phone (“smartphone”).

[0029] According to a third aspect of the invention, a biometric identification method is provided comprising the following steps: a) Transmit, via a biometric identification terminal, a biometric proof characteristic of an individual to an encoding terminal; b) Generate, by the encoding terminal, a biometric proof template using an encoding process according to any embodiment of the first aspect of the invention; c) Receive, via the biometric identification terminal, the biometric proof template; d) Compare, via the biometric identification terminal, the proof biometric template (GE-Bio) with at least one reference biometric template from a database of reference biometric templates.

[0030] According to some embodiments, the comparison step d) is performed according to an approximate search protocol, preferably an approximate search protocol based on a Hamming distance.

[0031] According to some embodiments, the biometric identification process further includes a step of generating, by the encoding terminal, a proof of encoding of the biometric proof template from the biometric proof characteristic, preferably a proof of encoding with zero disclosure of knowledge, and a step of verifying, by the biometric identification terminal, the proof of encoding.

[0032] According to some embodiments, the comparison step (d) is performed according to a method of data concealment and / or function concealment.

[0033] According to a fourth aspect of the invention, a biometric identification system is provided for implementing an identification method according to any one of the embodiments of the third aspect of the invention. In particular, a biometric identification system is provided comprising: - a biometric identification terminal including an acquisition device configured to acquire at least one biometric characteristic of a user; - a recording medium including a database of reference biometric templates; - an encoding terminal according to any one of the embodiments of the second aspect of the invention; the system being configured to execute the steps of a biometric identification process according to any one of the embodiments of the third aspect of the invention. Brief description of the drawings

[0034] [Fig.1] is a schematic representation of a biometric identification system comprising a biometric identification terminal and an encoding terminal.

[0035] [Fig.2] is a schematic representation of an identification terminal biometric.

[0036] [Fig.3] is a schematic representation of an encoding terminal.

[0037] [Fig.4] is an operating diagram of an identification terminal biometric according to a first embodiment.

[0038] [Fig.5] is an operating diagram of an encoding terminal according to a first embodiment.

[0039] [Fig.6] is an operating diagram of an encoding terminal according to a second embodiment

[0040] [Fig.7] is an operating diagram of an identification terminal biometric according to a first embodiment.

[0041] [Fig.8] is a flow diagram of an encoding process according to the invention.

[0042] [Fig.9] is a flow diagram of an encoding process according to a first mode of realization.

[0043] [Fig. 10] is a flow diagram of an encoding process according to a second embodiment.

[0044] [Fig. 11] a flow diagram of an encoding process according to a third embodiment Detailed description of the implementation methods

[0045] In this disclosure, embodiments are described in the general context of one or more hardware or devices capable of executing preloaded instructions such as, for example, computer-executable instructions for executing program modules. Program modules may include one or more routines, programs, objects, variables, commands, scripts, functions, applications, components, or data structures that can perform specific tasks or implement specific types of abstract data.

[0046] Certain embodiments can also be implemented in distributed computing environments where tasks are performed by remote data processing devices connected by a communication network. In a distributed computing environment, program modules may reside on local and / or remote computer storage media, including memory storage devices.

[0047] In the context of the invention, the term "biometric template" means any type of biometric data derived from one or more raw biometric characteristics following their processing by an algorithm, hereinafter referred to as encoding. The derived biometric data constituting the biometric template are generally distinct from the raw biometric data from which they are derived. Preferably, the biometric template conforms to the definition in ISO / IEC 19794-1:2011 Information technology — Biometric data interchange formats — Part 1: Framework.

[0048] With reference to [Fig.1], a biometric identification system 100 may include a biometric identification terminal 101 and an associated or user-specific encoding terminal 102 103. The biometric identification terminal 101 and the encoding terminal 102 are preferably configured to exchange data via a secure remote connection.

[0049] When a user 101 wishes to authenticate themselves with the biometric identification terminal 101 in order to access a resource or a restricted access area, they first submit an authentication request to said biometric identification terminal 101. According to a first example, the request can be submitted using a human-machine interface, "HMI", (not shown) with which the biometric identification terminal 101 is equipped. According to a second example, it can be submitted via the encoding terminal 103 through a remote connection, preferably a secure one.

[0050] Once the request is submitted, the biometric identification terminal 101 acquires a proof biometric characteristic of the user 103 using a suitable acquisition device and then transmits it to the encoding terminal 102. The biometric characteristic is generally chosen from among the dermatoglyphs of one or more fingers, palmar dermatoglyphs, one or more irises, or a face, or a combination thereof.

[0051] Upon receiving the biometric proof characteristic, the encoding terminal 102 generates a biometric proof template according to an encoding scheme, and then sends this biometric proof template to the biometric identification terminal 101. Once the biometric identification terminal 101 receives the biometric proof template, it compares it to one or more biometric templates of References are stored in a database. If there is a match between the biometric template being tested and at least one reference biometric template, user 103 is identified. They are then authorized to access the resource or access area. Otherwise, user 103 is not identified and access is denied. The biometric identification terminal 101 and / or the encoding terminal can notify the user of the success or failure of the identification process using a light signal, an audible signal, a message, or a combination thereof.

[0052] A 100 biometric identification system as described above can be used for the purpose of accessing one or more remote services, authorizing access to information stored in a collective or personal database, verifying the identity of one or more persons, retrieving login credentials, or retrieving one or more addresses of electronic money wallets such as a cryptocurrency.

[0053] An example 200 of a biometric identification terminal 101 is illustrated in [Fig.2]. The biometric identification terminal 200 comprises a physical acquisition module 201, a physical data processing module 202 and a protective housing 203.

[0054] The physical acquisition module 201 is in the form of a camera adapted for acquiring the image of one or more irises or a face. The protective housing 203a includes a transparent or semi-transparent window 203a to allow image acquisition by the acquisition module 201. Alternatively or in addition, the physical acquisition module 201 may include a device for acquiring a dermatoglyph of one or more fingers or a palmar dermatoglyph. On the surface of the protective housing 203, an acquisition area may be provided, leaving the active surface of said acquisition device exposed so that a user 103 can place one or more of their fingers and / or the palm of one of their hands on it.

[0055] The physical acquisition module 201 transmits the acquired data to the physical data processing module 202 by means of a connector 204. The physical data processing module 202 includes means for implementing biometric identification. It is responsible for automatically executing sequences of arithmetic or logical operations to perform tasks or actions. This module, commonly referred to as a computer, may include one or more central processing units (CPUs) 202a and / or one or more graphics processing units (GPUs) 202b, a physical remote communication module 202c, one or more physical input / output modules 202d for exchanging data with external devices, a transient storage medium 202e such as random access memory (RAM), a non-transient recording medium 202f, and buses of communication (not shown) for data transfer between internal components of module 202 data processing.

[0056] The physical data processing module 202 allows the execution of one or more program modules comprising instructions which, when the program module(s) are executed, cause the data processing module 202 to implement biometric identification. The program module(s) may be written in any programming language, compiled or interpreted. They may be part of a software solution, i.e., a collection of executable instructions, code, scripts, or other components, and / or databases.

[0057] Examples of biometric identification terminal 101 are described in the prior art, in particular in WO 2023 / 028221 Al [TOOLS FOR HUMANITY CORP [US] 02.03.2023, WO 2023 / 028242 Al [TOOLS FOR HUMANITY CORP [US] 01.03.2023 ; US 2008 / 253622 Al [RETICA SYSTEM INC [US]] 16.10.2008 ; US 2006 / 088193 Al [RETICA SYSTEM INC [US]] 24.07.2006 ; FR 3069681 Al [SAFRAN IDENTITY & SECURITY [FR]] 01.02.2019.

[0058] Figure 3 shows an example 300 of an encoding terminal 102 for implementing biometric identification. The encoding terminal 102, 300 is a mobile electronic device, preferably a multifunction mobile phone (“smartphone”). The encoding terminal 102, 300 comprises a protective upper housing 301, a protective lower housing, a physical data processing module 202, and a human-machine interface, “HMI”, 304 in the form of a touchscreen.

[0059] The data processing physical module 303 includes means for implementing biometric identification. It is responsible for automatically executing sequences of arithmetic or logical operations to perform tasks or actions. This physical module 303, commonly referred to as a computer, may include one or more central processing units (CPUs) 303a and / or one or more graphics processing units (GPUs) 303b, a remote communication physical module 303c, one or more input / output physical modules 303d for exchanging data with external devices, a transient storage medium 303e such as random access memory (RAM), a non-transient recording medium 303f, and communication buses (not shown) for transferring data between the internal components of the data processing module 303.It may also include a 303g secure element for storing cryptographic keys, executing encryption algorithms, and / or storing and / or encrypting any other algorithm and / or data whose security and confidentiality must be preserved.

[0060] The physical data processing module 303 allows the execution of one or more program modules comprising instructions which, when the or The program modules are executed, causing the data processing module 303 to implement biometric identification. The program module(s) can be written in any programming language, compiled or interpreted. They can be part of a software solution, that is, a collection of executable instructions, code, scripts, or other components, and / or databases.

[0061] Figures 400 and 500 respectively represent the operating diagrams of a 101, 200 biometric identification terminal and of a 102, 300 encoding terminal for the implementation of biometric identification.

[0062] With reference to [Fig.4], the biometric identification terminal 101, 200 may include a communication program module 401 (C-Mod), a proof biometric characteristic acquisition program module 402 (CE-Bio), a data entry program module 403 (LMod), a data processing program module 404 (T-Mod), a database 405 (BDD), and a validation program module 406 (V-Mod).

[0063] The data input program module 403 (LMod), the data processing program module 404 (T-Mod), and the validation program module 406 (V-Mod) can be implemented by the data processing physical module 202 of the biometric identification terminal 101, 200 described in [Fig. 2]. The communication program module 401 (C-Mod) and the proof biometric characteristic acquisition program module 402 (CE-Bio) can be implemented by the communication physical module 202c and the acquisition physical module 201 of said terminal 102, 200. The database 405 (DB) can be stored on the non-transient storage medium 202f of the data processing module 202.Alternatively, it can be stored in a non-transient electronic storage medium of a remote server with which the 101, 200 biometric identification terminal has established secure remote communication via, for example, the 401 communication program module (C-Mod).

[0064] With reference to [Fig.5], the encoding terminal 102, 300 can include a communication program module 501 (C-Mod), a data input program module 502 (LMod), an encoding module 503 (E-Mod) and a non-transient recording area 504.

[0065] The data input program module 502 (LMod) and the encoding program module 502 (E-Mod) can be implemented by the data processing physical module 303 of the encoding terminal 102, described in [Fig. 3]. The communication program module 501 (C-Mod) can be implemented by the communication physical module 303c. The non-transient recording area 504 can be implemented on the medium 202f. non-transient recording of the data processing module 202 and / or the secure element 303g.

[0066] The implementation of the biometric identification process briefly described in the context of [Fig.1] is now detailed with reference to [Fig.2] to 5.

[0067] The communication program module 301 of the terminal 101, 200 biometric identification is adapted for data exchange with remote electronic devices, such as the encoding terminal 102, 300, according to a secure remote connection. The secure connection is established by the 401 and 501 communication program modules of each of the terminals 101, 200, 102, and 300. When a biometric identification request is submitted to the biometric identification terminal 101 or 200, the encoding terminal 102 or 300 and the identification terminal 101 or 200 can exchange 401a and 501a identifiers (U-IDs). These identifiers enable unique identification of each of the terminals 101, 200, 102, and 300 for all subsequent exchanges, thus verifying the origin of the exchanged data. The 401a and 501a identifiers can contain any suitable type of data.Examples of identifiers could be a MAC address, a user ID 103, an EMEI number, a random number generated by each of the terminals 101, 200, 102, 300, or a combination thereof. Preferably, the data exchanged between the encoding terminal 102, 300 and the biometric identification terminal 101, 200 is encrypted using, for example, an asymmetric encryption protocol.

[0068] Once communication is established between the biometric identification terminal 101, 200 and the encoding terminal 102, 300, the biometric acquisition program module 402 of the biometric identification terminal 101, 200 acquires a proof biometric characteristic 402a (CE-Bio) and then transmits it to the encoding terminal 102, 300 via its communication program module 401 (C-Mod). The encoding terminal 102, 300 receives the proof biometric characteristic 402a (CE-Bio) via its communication program module 501 (C-Mod). The proof biometric characteristic 402a (CE-Bio) is transmitted to the data entry program module 502, and then to the encoding program module 503 (E-Mod).The 503 encoding program module (E-Mod) generates a 503a proof biometric template (GE-Bio) by encoding the 402a proof biometric characteristic (CE-Bio) according to a 504a encoding scheme (SE) recorded in the 504 non-transient recording area. The 503a proof biometric template (GE-Bio) is then transmitted to the 501 communication program module (C-Mod) for transmission to the 101, 200 biometric identification terminal.

[0069] The communication program module 401 (C-Mod) of the biometric identification terminal 101, 200 receives the proof biometric template 503a and transmits it to the processing program module 404 via the data entry program module 403 (LMod). The processing program module 404 (T-Mod) compares the proof biometric template 503a with one or more reference biometric templates 405a (GR-Bio) stored in a database 405. Each reference biometric template 405a (GR-Bio) is associated with a user 103. The database 405 can be stored in the non-transient electronic storage medium 202f specific to the biometric identification terminal 101, 200. Alternatively, it can be stored in a non-transient electronic storage medium of a remote server with which the 101,200 biometric identification terminal has established a secure remote communication.

[0070] The comparison of the 503a test biometric template (GR-Bio) with one or more 405a reference biometric templates (GR-Bio) is performed using any suitable method. For example, when the biometric templates are in the form of encoding vectors, the comparison may be a calculation of a matching score in the form of a dot product, a cross product, or a Euclidean distance between the representative vector of the 503a test biometric template (GE-Bio) and each of the representative vectors of the 405a reference biometric templates (GR-Bio).

[0071] According to certain embodiments, the comparison of the 503a proof biometric template (GE-Bio) with one or more 405a reference biometric templates (GR-Bio) is performed according to an approximate search protocol, preferably a fuzzy search protocol based on a Hamming distance. Approximate search is advantageously fast for comparing complex data, such as biometric templates, and / or when the number of biometric templates to be compared is large. An example of an implementation of a fuzzy search based on a Hamming distance is described in the article Galbraith & Zoberning (2019), "Obfuscated fuzzy hamming distance and conjunctions from subset product problems," Theory of Cryptography Conference.

[0072] According to certain preferred embodiments, the comparison of the proof biometric template 503a (GE-Bio) with one or more reference biometric templates 405a (GR-Bio) is performed using a method of data obfuscation and / or function obfuscation. Data obfuscation and / or function obfuscation renders programs and algorithms unintelligible while preserving their functionality or operability. In other words, within the scope of the invention, the manner in which the biometric template is compared A 503a proof (GE-Bio) with one or more 405a reference biometric templates (GR-Bio) is performed, remaining concealed from any third-party observer without prejudice to the result and performance of the comparison. Examples of implementing a data concealment method and / or function concealment are described in the articles Galbraith & Zoberning (2019), "Obfuscated fuzzy hamming distance and conjunctions from subset product problems," Theory of Cryptography Conference, and Barak et al. (2014) "Obfuscation for evasive functions," Theory of Cryptography Conference. Berlin, Heidelberg: Springer Berlin Heidelberg.

[0073] The validation program module 406 (V-Mod) determines whether the result(s) of the comparisons performed by the processing program module 405 (T-Mod) meet at least one validation criterion, in which case user 103 is identified. For example, when these results are matching scores, the validation criterion may be a threshold value against which the score values ​​are compared. If the value of at least one score is less than the threshold value, user 103 is considered identified. Conversely, if all the score values ​​are greater than the threshold value, the user is not identified and access is denied by the biometric identification terminal 101, 200.

[0074] The validation program module 406 (V-Mod) can generate an authentication variable 406a (Auth), for example a Boolean variable, depending on whether the identification is successful (Auth = TRUE) or not (Auth = False). The value of the authentication variable 406a can be transmitted to the communication program module 401 to inform the user 103 of the success or failure of the identification via the communication program module 501 (C-Mod) of the encoding terminal 102, 300.

[0075] With reference to [Fig. 6], the encoding terminal 102, 300 may include a program module 601 for generating an encoding proof (EP) 601a of the proof biometric template 503a (GE-Bio) from the proof biometric characteristic 402a (CE-Bio) transmitted by the biometric identification terminal 101, 200. After generation, this encoding proof (EP) 601a is transmitted to the biometric identification terminal 101, 200 along with the proof biometric template 503a (GE-Bio). With reference to [Fig. 7], the biometric identification terminal 101, 200 may include a program module 701 for verifying the received encoding proof (EP).

[0076] The function of the encoding proof is to allow the biometric identification terminal 101, 200 to verify that the proof biometric template was indeed generated by the encoding terminal 102, 300 from the proof biometric characteristic that transmitted it, and not from other data. Preferably, the encoding proof is a zero-knowledge proof.

[0077] By way of example, when the 802, 902, 1002, 1102 encoding scheme (SE) is implemented in particular as a neural network in accordance with the embodiments described below, a zero-knowledge disclosure proof can be generated according to the method described in South et al. (2024) "Verifiable evaluations of machine learning models using zkSNARKs." ​​arXiv preprint arXiv:2402.02675.

[0078] According to the invention, with reference to [Fig.8], the encoding terminal 101, 300 includes means for implementing a method 800 for encoding a biometric proof template 803, said method takes, as input data, a biometric proof characteristic 801 (CE-Bio), and provides, as output data, a biometric proof template 803 (GE-Bio), in which the biometric proof template 803 (GE-Bio) is generated from the biometric proof characteristic 801 (CE-Bio) according to an encoding scheme 802 (SE) representative of the distance 802a (d(CE-Bio, CR-Bio)) according to a metric between the biometric proof characteristic 801 (CE-Bio) and a reference biometric characteristic 802b (CR-Bio).

[0079] Thanks to the encoding process 800 according to the invention, the biometric template 803 of proof (GE-Bio) generated by the encoding terminal 101, 300 is further removed from any biometric reference template (GR-Bio) to which it may be compared later, the further removed the biometric characteristic 801 of proof (CE-Bio) from which it was generated is from the biometric characteristic 802b of reference (CR-Bio). Thus, the risk of false acceptance is considerably reduced because the further the 801 test biometric characteristic (CE-Bio) is from the 802b reference biometric characteristic (CR-Bio), the more the 803 test biometric template (GE-Bio) is altered compared to a situation in which the 801 test biometric characteristic (CE-Bio) would be identical or close to the 802b reference biometric characteristic (CR-Bio).

[0080] By way of illustration, in the context of a biometric identification process illustrated by [Figs. 1] to 5, a user 103 presents themselves at a biometric identification terminal 101, 200 to access a resource. Assuming that user 103 is an identity thief, they are equipped with an encoding terminal 102, 300 that they stole from a third party and attempt to impersonate that person. The reference biometric characteristic 802b (CR-Bio) used in the identification process 800 implemented by the stolen encoding terminal 102, 200 is that of the third party.

[0081] The biometric identification terminal 101, 200 acquires a proof biometric characteristic 402a (CE-Bio) from the imposter user 103 and transmits it to the encoding terminal 102, 300. The encoding terminal 102 receives the proof biometric characteristic 402a, 801 (CE-Bio) and generates a template. biometric 503a, 803 test (GE-Bio) according to its 802 encoding scheme (SE), that is to say representative of a distance 802a (d(CE-Bio, CR-Bio)) according to a metric between the biometric characteristic 402a, 801 test (CE-Bio) of the usurper 103 and the biometric characteristic 802b reference (CR-Bio) of the third person to whom the terminal 102, 300 encoding belongs. Since the 402a, 801 proof biometric characteristic (CE-Bio) of the imposter user 103 is different from that of the third-party owner, the 102, 300 encoding terminal generates a 503a, 803 proof biometric template (GE-Bio) completely different from the one it would have generated if user 103 had been the third party.

[0082] Once the 503a, 803 proof biometric template (GE-Bio) is generated, the 102, 300 encoding terminal transmits it to the 101, 200 biometric identification terminal. The latter compares it to the 405a reference biometric templates (GR-Bio) in a 405 database, and fails to establish a match with a 405a reference biometric template (GR-Bio) of the third party registered in the 405 database.

[0083] In other words, the encoding method 800 according to the invention makes it possible to disguise any "authentic" proof biometric template (GE-Bio) that could be generated from a proof biometric characteristic (CR-Bio) similar to the reference biometric characteristic (CR-Bio), as long as the proof biometric characteristic (CE-Bio) does not correspond to said reference biometric characteristic (CR-Bio). In particular, this disguise is achieved by generating a proof biometric template (GE-Bio) that is all the more random the more the proof biometric characteristic (CE-Bio) differs from the reference biometric characteristic (CR-Bio).

[0084] The encoding method 800 according to the invention is implemented by one or more program modules, in particular by the encoding program module 503 of the encoding terminal 102, 300. The program module(s) are executed by the data processing module 303 of the encoding terminal 102, 300. All or part of these modules may be executed by a secure element 303g of the physical data processing module 303.

[0085] It should be emphasized here that the 802 encoding scheme (ES) is based on a metric 802a (d(CE-Bio, CR-Bio)) distance between the 801 proof biometric feature (CE-Bio) and a 802b reference biometric feature (CR-Bio). In other words, the metric distance is a distance between the raw data of the 801 proof biometric feature (CE-Bio) and the 802b reference biometric feature (CR-Bio). Optionally, the raw data may undergo digital preprocessing, such as noise reduction, edge detection, or cropping, without that the information they constitute is modified as in the case of a biometric template generation.

[0086] Equivalently, with reference to [Fig.9], the encoding scheme 902 (SE) can be based on a distance 902a d(GEI-Bio, GIR-Bio) between an internal biometric template 902c of reference (GIR-Bio) generated from the biometric characteristic 902b of reference (CR-Bio) and an intermediate biometric template 902d of proof (GEI-Bio) generated from the biometric characteristic 901 of proof (CE-Bio). In these embodiments, the encoding scheme 902 (ES) may include a pre-encoder 902e (P-Enc) configured to generate a reference internal biometric template 902c (GIR-Bio) generated from the reference biometric characteristic 902b (CR-Bio) and an intermediate proof biometric template 902d (GEI-Bio) generated from the proof biometric characteristic 901 (CE-Bio). The encoding terminal 102,300 then generates a proof biometric template 903 (GE-Bio) based on this distance 902a in accordance with the encoding scheme 902 (ES)..

[0087] The 902e pre-encoder (P-Enc) can be a generic prior art encoder. For example, in the case of a biometric feature consisting of one or more images of a user 103, it can be a pre-encoder such as described in Hasnat et al. (2017) "Deepvisage: Making face recognition simple yet with powerful generalization skills." Proceedings of the IEEE International Conference on Computer Vision Workshops. The internal reference biometric template 902c (GIR-Bio) generated from the reference biometric feature 902b (CR-Bio) and the intermediate proof biometric template 902d (GEI-Bio) generated from the proof biometric feature 901 (CE-Bio) are generally in vector form.

[0088] The distance metric between the proof biometric characteristic 901 (CE-Bio) and the reference biometric characteristic 902b (CR-Bio) and / or between a reference internal biometric template 902c (GIR-Bio) generated from the reference biometric characteristic 902b (CR-Bio) and an intermediate proof biometric template 902d (GEI-Bio) generated from the proof biometric characteristic 901 (CE-Bio) is of any suitable type. In particular, it may be a dot product, a cross product, a Euclidean distance, or a Hamming distance.

[0089] According to certain embodiments, with reference to [Fig. 10], the encoding scheme (ES) 10002 comprises an F-Trans transition function 1002f or an F-Dist distribution function 1002a centered on the distance d(GEI-Bio, GIR-Bio) according to a metric between an internal reference biometric template 1002c (GIR-Bio) generated from the reference biometric characteristic 1002b (CR-Bio) and a intermediate biometric template 1002b of proof (GEI-Bio) generated from the biometric characteristic 1001 of proof (CE-Bio).

[0090] By way of example of an embodiment, a 1002f F-Trans transition function of the 1002 encoding scheme can be expressed using the following formula: GE = fTrms(GEIGEI.GIR) x GIR

[0091] Where GE is the trial biometric template (GE-Bio), GEI is an intermediate trial biometric template 1002b (GEI-Bio) generated from the trial biometric characteristic 1001 (CE-Bio), GIR is an internal reference biometric template 1002c (GIR-Bio) generated from the reference biometric characteristic 1001 (CR-Bio), GEI.GIR is the dot product between GEI and GIR and represents the distance 1002a d(GEI-Bio, GIR-Bio) between the intermediate trial biometric template 1002b (GEI-Bio) and the internal reference biometric template 1002c (GIR-Bio). The function h is a decreasing function such that: h: [0,1] -* [0,1], x 1 if x = 1 limA(x) =0 x-*0

[0092] When the intermediate biometric template 1002d of testing (GEI-Bio) generated from the biometric characteristic 1001 of testing (CE-Bio) is close to the internal biometric template 1002c of reference (GIR-Bio) generated from the biometric characteristic 1002b of reference (CR-Bio), in other words when the biometric characteristic 1001 of testing (CE-Bio) and the biometric characteristic 1002b of reference (CR-Bio) belong to the same user 103, their scalar product tends towards unity. The encoding scheme (SE) 1002 generates, via the F-Trans transition function 1002f, a proof biometric template 1003 (GE-Bio) similar to, or even identical to, the reference biometric template 405a (GR-Bio) expected by the biometric identification terminal 101,200. However, if the intermediate proof biometric template 1002d (GEI-Bio) and the internal reference biometric template 1002c (GIR-Bio) do not match, the dot product tends towards zero.The 1002 encoding scheme (SE) generates, via the 1002f F-Trans transition function, a 1003 proof biometric template (GE-Bio) very different from the 405a reference biometric template (GR-Bio) expected by the 101,200 biometric identification terminal.

[0093] In order to increase the level of security, and thus reduce the risk of false acceptance, it may be advantageous to increase the degree of dissimilarity of the reference biometric characteristic in the event of identity theft. According to some advantageous embodiments, with reference to [Fig. 11], the encoding scheme 11002 (SE) further includes a noise generation function 11002g, preferably a noise generation function taking, as input variable, the biometric characteristic 11001 of proof (CE-Bio).

[0094] By way of example of an embodiment, a noise generation function 1102g F-Br taking, as input variable, the biometric proof characteristic 11001 (CE-Bio) can be expressed using the following formula: A (GEI) - (1-f (GE!)) xg(CE) J Brv ' \ J Tram\ H °

[0095] Where CE is the proof biometric characteristic 11001 (CE-Bio), GEI is an intermediate proof biometric template 1102d (GEI-Bio) generated from the proof biometric characteristic 1101 (CE-Bio), f-trans is a transition function, and g is a function generating a random number from the proof biometric characteristic 1101 (CE-Bio). The function g can be a hash function, a weighted summation function, or a reduction function.

[0096] From the example of the transition function f-Trans according to the previous example, the biometric template 11003 of proof (GE-Bio) generated, by the encoding terminal 102, 200, according to the encoding scheme 11002 (SE), can be expressed according to the following relation: GE = fTram (GEI) + fBr (GEI) = (GEI.GIR) x GIR + (I - ((GEI.GIR) x GIR)) xg (CE),

[0097] In the embodiments described above, the 802, 902, 1002, 1102 encoding scheme (SE) and / or the 802b, 902b, 1002b, 1102b reference biometric characteristic (CR-Bio) are stored, preferably in encrypted form, in the non-transient recording medium 302f of the data processing physical module 303 of the encoding terminal 102, 300. They may also be recorded in a secure element 303a of the data processing physical module 303 of the encoding terminal 102, 300. The encoding program module(s) 503 (E-mod) may be executed within this secure element 303a.

[0098] It is still possible to increase the level of security by preventing any possibility for an imposter or fraudster to reconstruct the 802, 902, 1002, 1102 encoding scheme (ES) and / or the 802b, 902b, 1002b, 1102b reference biometric characteristic (CR-Bio) by analyzing the results of brute-force tests and a heuristic approach such as a trial-and-error method. To this end, according to advantageous embodiments, the 802, 902, 1002, 1102 encoding scheme (ES) is implemented as a neural network previously trained according to a teacher-student protocol.

[0099] Thus, a neural network can be pre-trained according to a teacher-student protocol to learn to reproduce the outputs of the transition, distribution and / or noise functions described in the preceding embodiments as well as the 802b, 902b, 1002b, and 1102b distances between the test (CE-Bio) and reference (CR-Bio) biometric characteristics and / or the intermediate test (GEI-Bio) and reference (GIR-Bio) biometric templates. An example of a neural network trained using a teacher-student protocol is described in the article Papernot et al. (2016) "Semi-supervised knowledge transfer for deep learning from private training data." arXiv preprint arXiv: 1610.05755. This approach also has the advantage of being able to use a neural network with a less complex structure than a classical neural network, that is, a neural network designed from scratch to implement the encoding scheme without training using a teacher-student protocol. The execution of the 802, 902, 1002, 1102 encoding scheme (SE) is then faster and more accurate.

[0100] According to some examples, the learning method for such a neural network can also be based on a loss function whose parameters are adjusted so that the neural network provides a test biometric template (GE-Bio) that is more faithful to the reference biometric characteristic (CR-Bio) the closer the test biometric characteristic (CE-Bio) is to it. In particular, it may be advantageous to use a fine-tuning approach whereby a neural network previously trained to provide a test biometric template (GE-Bio) from a test biometric characteristic (CE-Bio) is specialized on the reference biometric characteristic (CR-Bio).

[0101] According to some examples, the neural network can also be trained on the basis of several reference biometric characteristics (RB-Bio) of the same nature in order to increase the sensitivity of the neural network. Multiple acquisitions of the same reference biometric characteristic (RB-Bio) of the user 103 can then be made, on the basis of which the neural network is trained via the encoding scheme (ES) that it must reproduce.

[0102] According to preferred embodiments, the 802, 902, 1002, 1102 encoding scheme (SE) is specific to the biometric encoding terminal 102, 300. The 802, 902, 1002, 1102 encoding scheme (SE) then differs from one encoding terminal 102, 300 to another, introducing an additional degree of diversity during the generation of the proof biometric template (GE-Bio) when the proof biometric characteristic (CE-Bio) deviates from the reference 802b biometric characteristic (CR-Bio). In other words, to put it more figuratively, each 102, 300 encoding terminal, via its own encoding scheme, "camouflages" or "conceals" the reference biometric characteristic (CR-Bio) more in its own way than the test biometric characteristic (CE-Bio) differs from it.

[0103] According to certain embodiments, the reference biometric characteristic (CR-Bio) is specific to the user 103 of the encoding terminal 102, 300. In particular, When user 103 owns encoding terminal 102, 300, the reference biometric characteristic (CR-Bio) is exclusively that of said user 103. For example, encoding terminal 102, 300 is a mobile electronic device, such as a multifunction phone, owned solely by user 103. The reference biometric characteristic (CR-Bio) is then a reference biometric characteristic (CR-Bio) of user 103. References Literature patent

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Claims

Demands

1. A method (800, 900, 1000, 11000) for encoding, implemented by an encoding terminal (102, 300), a biometric template (803, 903, 1003, 1103) for proof (GE-Bio), said method takes as input data a biometric characteristic (801, 901, 1001, 11001) for proof (CE-Bio), and provides as output data a biometric template (803, 903, 1003, 1103) for proof (GE-Bio), in which the biometric template (803, 903, 1003, 1103) for proof (GE-Bio) is generated from the biometric characteristic (801, 901, 1001, 1101) of test (CE-Bio) according to an (802, 902, 1002, 1102) encoding scheme (SE) representative of the distance (802a, 902a, 1002a, 11002a), d(CE-Bio, CR-Bio), according to a metric between the biometric characteristic (801, 901, 1001, 1101) of test (CE-Bio) and a biometric characteristic (802b, 902b, 1002b, 1102b) of reference (CR-Bio).

2. Encoding method (900) according to claim 1, wherein the encoding scheme (902) (SE) comprises a pre-encoder (902e) (P-Enc) configured to generate an internal biometric template (902c) of reference (GIR-Bio) generated from the biometric characteristic (902b) of reference (CR-Bio) and an intermediate biometric template (902d) of proof (GEI-Bio) generated from the biometric characteristic (901) of proof (CE-Bio), the distance according to a metric is a distance (902a), d(CE-Bio, CR-Bio) between the internal biometric template (902c) of reference (GIR-Bio) and the intermediate biometric template (902b) of proof (GEI-Bio).

3. Encoding method (1000) according to any one of claims 1 to 2, wherein the encoding scheme (1002) comprises a transition function (1002f) (F-Trans) or a distribution function (F-Dist) centered on the distance (1002a) d(GEI-Bio, GIR-Bio) according to a metric between an internal biometric template (1002c) of reference (GIR-Bio) generated from the biometric characteristic (1002b) of reference (CR-Bio) and an intermediate biometric template (1002b) of proof (GEI-Bio) generated from the biometric characteristic (1001) of proof (CE-Bio).

4. Encoding method (11000) according to any one of claims 1 to 3, wherein the encoding scheme (11002) (SE) further comprises a noise generation function (11002g) (F- Br), preferably a noise generation function taking, as input variable, the biometric characteristic (11001) of proof (CE-Bio).

5. Encoding method (11000) according to claim 4, wherein the noise generation function (11002g) (F-Br) comprises a function generating a random number from the proof biometric characteristic 1101 (CE-Bio) selected from a hash function, a weighted summation function or a reduction function.

6. Encoding method (800, 900, 1000 11000) according to any one of claims 1 to 5, wherein the encoding scheme (802, 902, 1002, 1102) (SE) is implemented in the form of a neural network previously trained according to a teacher-student protocol.

7. Encoding method (800, 900, 1000 11000) according to any one of claims 1 to 6, wherein the encoding scheme (802, 902, 1002, 1102) (SE) is specific to the biometric terminal (102, 300).

8. Encoding method (800, 900, 1000 11000) according to any one of claims 1 to 6, wherein the reference biometric characteristic (802b, 902b, 1002b, 1102b) (CR-Bio) is user-specific (103) of the encoding terminal (102, 300).

9. Encoding terminal (103, 300) comprising means for implementing the encoding method (800, 900, 1000, 1100) according to any one of claims 1 to 8.

10. Encoding terminal (103, 300) according to claim 9, such that it is a mobile electronic device, preferably a multifunction mobile phone.

11. A biometric identification method comprising the following steps: a) Transmitting, via a biometric identification terminal (101, 200), a proof biometric characteristic (402a) (CR-Bio) of an individual (103) to an encoding terminal (102, 300); b) Generating, via the encoding terminal (102, 300), a proof biometric template (503a) (GR-Bio) using an encoding method (800, 900, 1000, 11000) according to any one of claims 1 to 8; c) Receiving, via the biometric identification terminal (101, 200), the proof biometric template (503a) (GR-Bio); d) Compare, by the biometric identification terminal (102, 300), the biometric template (503a) of the test (GE-Bio) with at least one biometric template (405a) of reference (GR-Bio) from a database (405) of reference biometric templates (GR-Bio).

12. Identification method according to claim 11, wherein the comparison step d) is performed according to an approximate search protocol, preferably an approximate search protocol based on a Hamming distance.

13. Identification method according to any one of claims 11 to 12, further comprising a step of generating, by the encoding terminal (102, 200), an encoding proof (601a) of the biometric template (503a) of proof (GE-Bio) from the biometric characteristic (402a) of proof (PE), preferably an encoding proof with zero disclosure of knowledge, and a step of verifying, by the biometric identification terminal (101, 200), the encoding proof (601a) (PE).

14. A method according to any one of claims 11 to 13, wherein the comparison step (d) is performed according to a method of data concealment and / or function concealment.

15. A biometric identification system (100) comprising: - a biometric identification terminal (101, 200) including an acquisition device (201) configured to acquire at least one proof biometric characteristic (402a) (CR-Bio) of a user (103); - a recording medium (202f) including a database (BDD) of reference biometric templates (405a) (GR-Bio); - an encoding terminal (102, 300) according to any one of claims 9 to 10; the system being configured to perform the steps of an identification process according to any one of claims 11 to 14.

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