EP4078437A1 - Enrolement par empreinte digitale sur une carte a puce - Google Patents
Enrolement par empreinte digitale sur une carte a puceInfo
- Publication number
- EP4078437A1 EP4078437A1 EP20851214.5A EP20851214A EP4078437A1 EP 4078437 A1 EP4078437 A1 EP 4078437A1 EP 20851214 A EP20851214 A EP 20851214A EP 4078437 A1 EP4078437 A1 EP 4078437A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- mode
- fingerprint
- enrollment
- smart card
- fingerprints
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/34—User authentication involving the use of external additional devices, e.g. dongles or smart cards
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/13—Sensors therefor
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/13—Sensors therefor
- G06V40/1318—Sensors therefor using electro-optical elements or layers, e.g. electroluminescent sensing
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1347—Preprocessing; Feature extraction
- G06V40/1353—Extracting features related to minutiae or pores
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1365—Matching; Classification
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1365—Matching; Classification
- G06V40/1371—Matching features related to minutiae or pores
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/50—Maintenance of biometric data or enrolment thereof
Definitions
- the present invention relates to fingerprint authentication, and more particularly relates to an enrollment implemented by a smart card to allow subsequent authentication of a user.
- a smart card can use biometric data to authenticate a user, the result of this authentication making it possible, for example, to validate or reject a transaction implemented by this smart card.
- fingerprint verification can securely authenticate the user of a smart card.
- a smart card can acquire a fingerprint by means of any digital sensor and compare this fingerprint with a reference fingerprint model which is previously generated and stored in a memory. of the smart card during a so-called enrollment step. By verifying whether the acquired fingerprint and the reference fingerprint pattern match, the smart card can determine whether the authentication is successful or not.
- the reference fingerprint template pre-recorded in the smart card is as faithful as possible to the user's finger (s).
- the enrollment step during which the user registers his fingerprint is therefore critical.
- the complexity of the enrollment step varies depending on the case and depends in particular on the sensitivity of the fingerprint sensor, the number of fingerprints to be acquired by the smart card and, more generally, the configuration of the smart card and the digital sensor in question. It is not always easy for a user to properly enroll one or more fingers, particularly due to the varying complexity of the procedure.
- Climatic conditions humidity, temperature, etc.
- the constraints in which the enrollment step takes place can in particular be strongly linked to the often limited resources available to the smart cards.
- the present invention relates to a method of enrollment (or processing) by fingerprint implemented by a smart card comprising a memory, the method comprising the execution of a first enrollment using a power supply supplied. by a power source internal or external to said smart card, said first enrollment comprising:
- N N first fingerprints in the form of image pixels representative of said fingerprints, N being an integer greater than or equal to 2;
- the first enrollment further comprising:
- the applied mode determination of a mode, called the applied mode, in which the first enrollment is carried out, the applied mode being a first mode if the power supply is below a predefined threshold and being a second mode if the power supply is greater or equal to the predefined threshold;
- the present invention advantageously enables the smart card to reliably and efficiently authenticate a user by means of fingerprints. Thanks to the invention, the smart card can perform fingerprint authentication optimally despite the varying contexts in which a user and the smart card are likely to find themselves. This is in particular possible because the smart card of the invention can generate the richest and most complete fingerprint model possible, within the limits imposed by the power supply (and therefore processing) resources available to the smart card. to complete the enrollment.
- the method comprises:
- the electronic device acquires the fingerprints by means of a fingerprint sensor on board said electronic device or by cooperating with an external device comprising a fingerprint sensor.
- the method comprises:
- the smart card comprising at least one communication interface for cooperating with at least one external device, the smart card being able to receive via said at least one communication interface an electrical supply from said at least an external device serving as a power source; wherein the smart card determines the mode applied during said first enrollment based on the use of said at least one communication interface.
- the method comprises:
- the characteristic points include fingerprint minutiae.
- the number N of first fingerprints obtained to carry out the first enrollment is adapted so as to be higher in the second mode than in the first mode, the quantity of aggregated digital data to generate the model of fingerprint being a function of the number N.
- the predetermined level of resolution applied during the analysis is adapted so as to be higher in the second mode than in the first mode, the quantity of digital data aggregated to generate the fingerprint model. being a function of said predetermined resolution level.
- said analysis comprises:
- said evaluation of a color of each selected pixel is carried out on the basis of a reading of said selected pixel and of a reading of X neighboring pixels of said selected pixel, X being an integer greater than or equal to 0 which is adapted so as to be higher in the second mode than in the first mode.
- said evaluation comprises the generation for each selected pixel of a respective color encoded according to a predetermined encoding level, the predetermined encoding level being adapted so as to be of better quality in the second mode than in the first mode, the digital data being extracted from the colors encoded for each selected pixel.
- the smart card acquires and analyzes the N first fingerprints in a predetermined time range, said time range being adapted so as to be greater in the second mode than in the second mode. first mode.
- the first enrollment comprises:
- the predetermined minimum level of coverage being adapted so as to be higher if no initial enrollment, other than the first enrollment, was carried out prior to the first enrollment only in the opposite case; wherein the fingerprint template is generated from the fingerprint data extracted from each first fingerprint for which the level of coverage reaches the predetermined minimum level of coverage.
- the first enrollment is carried out on detection that an authentication has passed successfully beforehand.
- the first enrollment is performed on detection that an authentication has passed successfully beforehand, the authentication comprising:
- the first fingerprints obtained comprise at least one fingerprint selected from the second fingerprints acquired during said authentication.
- the authentication comprises:
- the different steps of the enrollment process are determined by computer program instructions.
- the invention is also aimed at a computer program on an information medium (or recording medium), this program being capable of being implemented in a smart card and more generally in a computer, this program comprising instructions adapted to the implementation of the steps of an enrollment method as defined in this document.
- This program can be made up of several sub-parts stored in the same memory or in separate memories.
- This program can use any programming language, and be in the form of source code, object code, or intermediate code between source code and object code, such as in a partially compiled form, or in any other. desirable form.
- the invention also relates to an information medium (or recording medium) readable by the smart card of the invention, and more generally by a computer, and comprising instructions of a computer program as defined in this document.
- the information medium can be any entity or device capable of storing the program.
- the medium may include a storage means, such as a rewritable non-volatile memory or ROM, for example a CD ROM or a microelectronic circuit ROM, or else a magnetic recording means, for example a floppy disk (floppy say) or a hard drive.
- the information medium can be a transmissible medium such as an electrical or optical signal, which can be conveyed via an electrical or optical cable, by radio or by other means.
- the program according to the invention can in particular be downloaded from an Internet type network.
- the information medium can be an integrated circuit in which the program is incorporated, the circuit being adapted to execute or to be used in the execution of the method in question.
- the present invention also relates to a corresponding smart card, configured to implement the enrollment method of the invention. More specifically, the invention relates to a smart card configured to perform enrollment by fingerprint using a power supply supplied by a power source internal or external to said smart card, said smart card comprising: - a memory ;
- a determination module configured to determine a mode, called the applied mode, in which the first enrollment is carried out, the applied mode being a first mode if the power supply is below a predefined threshold and being a second mode if the power supply electrical is greater than or equal to the predefined threshold;
- an obtaining module configured to obtain N first fingerprints in the form of image pixels representative of said fingerprints, N being an integer greater than or equal to 2;
- an analysis module configured to perform an analysis of said N first fingerprints, during which finger data representative of characteristic points of said fingerprints are extracted by reading the image pixels at a predetermined resolution level;
- a generation module configured to generate a fingerprint model by aggregating at least said digital data extracted from the N first fingerprints, in which the determination module is further configured to adapt, depending on the mode applied, at least one of said number N and said level of resolution, said at least one parameter being set to be higher in the second mode than in the first mode so that the fingerprint model exhibits a higher level of definition in the second mode than in the first mode.
- the invention is implemented by means of software and / or hardware components.
- module can correspond in this document both to a software component, to a hardware component or to a set of hardware and software components.
- a software component corresponds to one or more computer programs, one or more sub-programs of a program, or more generally to any element of a program or of a software capable of implementing a function or a set of functions, as described in this document for the module concerned.
- a hardware component corresponds to any element of a hardware assembly capable of implementing a function or a set of functions, according to what is described in this document for the module concerned. It may be a programmable hardware component or with an integrated processor for executing software, for example an integrated circuit, a smart card, a memory card, an electronic card for executing a firmware ( firmware), etc.
- FIG. 1 schematically represents the structure of a smart card as well as the environment in which it interacts, in accordance with a particular embodiment of the invention
- FIG. 2 schematically represents modules implemented by the smart card, according to a particular embodiment of the invention
- FIGS. 3A, 3B and 3C schematically represent predefined rules applied by the smart card, according to particular embodiments of the invention.
- FIG. 4 schematically represents, in the form of diagrams, the steps for implementing an enrollment method according to a particular embodiment of the invention
- FIG. 5 schematically represents the analysis of a fingerprint, according to a particular embodiment of the invention
- FIG. 6 schematically represents the analysis of a fingerprint, according to a particular embodiment of the invention
- FIG. 7 schematically represents, in the form of diagrams, the steps for implementing an enrollment method according to a particular embodiment of the invention.
- the invention proposes according to various embodiments to allow the authentication of a user of a smart card from his fingerprints.
- the invention provides in particular an enrollment during which a smart card acquires fingerprints and analyzes them so as to extract therefrom digital data which are used to generate a fingerprint model. digital.
- This model fingerprint can thus be stored by the smart card and then used later as a reference fingerprint to verify the validity of a fingerprint of a user wishing to authenticate with the smart card.
- the smart card is in particular able to operate according to two distinct operating modes - hereinafter denoted first and second mode MD1, MD2 - as a function of the level of electrical supply (or of electrical energy) that it receives during enlistment.
- the smart card determines one or more parameters to be applied during enrollment according to the mode applied (that is to say according to the mode in which the smart card is located during said enrollment, and therefore according to the power level it receives), so that the fingerprint pattern has a higher definition (or quality) in the second mode (high power supply) than in the first mode (low power supply).
- the invention aims in particular to adapt the quality of the fingerprint model generated by the smart card according to the context in which it is found, and more particularly according to the power supply (the electrical energy received) at its disposal to carry out the analysis processing necessary on the fingerprints acquired to produce the fingerprint model.
- the invention aims to optimize the use of its resources as a function of the power source (internal and / or external) which it currently has at the time of enrollment, in order to generate a fingerprint model of the best possible quality in view of the power resources (and therefore processing) of which she disposes.
- an “electrical power supply” designates electrical energy supplied in any suitable form (current, voltage, power), by an electrical power source (also called a power source or a source of electrical energy). , to the device to be supplied (that is to say to the device of the invention in the embodiments which follow).
- a power supply can be expressed, for example, in terms of power supplied, voltage supplied or current supplied.
- the invention can be applied to any smart card, such as in particular a bank card (or payment card), a transport card, an access card, a health insurance card, an identity card. , a voting card, a driving license card, etc., capable of carrying out any transaction (for example a transaction for carrying out an electronic vote, a transaction allowing access to identity data of the electronic device, a transaction to gain physical or logical access, etc.).
- the invention can be applied more generally to electronic devices other than smart cards, capable of processing transactions, such as, for example, terminals (smartphone, tablet, etc.).
- terminals smartphone, tablet, etc.
- the invention can be applied, for example, to terminals implementing a payment application to process a payment transaction in cooperation with an external terminal.
- the invention applies to bank cards of the EMV type (for "Europay MasterCard Visa") or using other types of protocols.
- FIG 1 schematically shows a CD1 smart card according to a particular embodiment of the invention.
- This CD1 smart card is configured to implement an enrollment method (or processing method) according to one of the embodiments described below with particular reference to Figures 3-7.
- the smart card CD1 is a bank card (or payment card).
- This smart card can have an ID-1 format specified in the ISO / IEC 7810 standard.
- the CD1 smart card can also be a smart card with contacts (the characteristics of which are detailed in the ISO / IEC 7816 standard) and / or a contactless smart card (the characteristics of which are detailed in the ISO / IEC 14443 or NFC / ISO 15693 standard).
- the smart card CD1 is for example configured to process payment transactions according to the EMV protocol.
- the smart card CD1 comprises a processor 2, a volatile memory (RAM) MR1, a rewritable non-volatile memory MR2, a fingerprint sensor (or fingerprint reader) 4, a source internal power supply SCO and communication interfaces INT1 and INT2.
- processor 2 a volatile memory (RAM) MR1, a rewritable non-volatile memory MR2, a fingerprint sensor (or fingerprint reader) 4, a source internal power supply SCO and communication interfaces INT1 and INT2.
- the internal components of the smart card CD1 are controlled by the processor 2, for example by means of a data bus.
- the processor 2 can use the volatile memory MR1 to temporarily store data generated during its operation, in particular to carry out an enrollment (for example example for temporarily storing fingerprints FG1 acquired (or obtained) by the smart card CD1 as well as digital data DT1 generated during an enrollment from acquired fingerprints).
- the rewritable non-volatile memory MR2 (for example of Flash or EEPROM type) constitutes a recording medium (or information medium) in accordance with a particular embodiment, readable by the processor 2, and on which is recorded a first program computer PG1 according to a particular embodiment.
- the first program PG1 can be recorded in a read only memory (ROM) (not shown) of the smart card CD1.
- the computer program (or application) PG1 include instructions for executing the steps of an enrollment method according to a particular embodiment, for example at least one of the example methods described below.
- the memory MR2 can also be used to store a fingerprint model ML1, predefined rules RL1 as well as PT parameters applied by the card to carry out an enrollment.
- the PT parameters in particular can be recorded in another memory of the smart card CD1, for example in the volatile memory MR1 during the execution of an enrollment.
- the smart card CD1 comprises a fingerprint sensor 4 allowing the acquisition of FG1 fingerprints of a user UR, in particular during an enrollment, then subsequently when a user wishes to s '' authenticate with the CD1 smart card.
- a fingerprint sensor in the CD1 smart card is not mandatory.
- the smart card CD1 is configured to acquire fingerprints from an external terminal (for example T1 and / or T2 as described below) with which it cooperates, this external terminal (or device) comprising or using such a fingerprint sensor.
- the smart card CD1 also comprises an internal electrical power supply source SCO capable of delivering an electrical power supply ALO (electrical energy) to the smart card CD1.
- This internal source SCO can thus be any battery on board the smart card CD1, such as by example a supercapacitor or a rechargeable battery, other examples being however possible. Note that variants are also possible in which the smart card CD1 has no internal power source.
- the smart card CD1 is further configured to connect to at least one external power supply source, the type may vary depending on the case. The way in which the smart card CD1 collects the electrical energy supplied from the outside may vary depending on the case (transmission by contact or contactless, by induction, etc.).
- the smart card CD1 comprises two communication interfaces INT1 and INT2 making it possible to communicate respectively with two external terminals (or devices) denoted T1 and T2, respectively.
- the smart card CD1 is able in this example to cooperate with one of the two terminals T1 and T2 to carry out the method of the invention.
- the number and nature of the external devices with which the CD1 smart card can be coupled to carry out the invention may vary depending on the case.
- the communication interfaces INT1 and INT2 can be contact or contactless communication interfaces.
- the smart card CD1 has only one communication interface for communicating with the outside.
- the smart card CD1 is configured to communicate in contactless mode with the terminal T1 via the communication interface I NT 1.
- the interface INT1 includes an RF antenna for communicating in contactless mode. , for example according to the ISO / IEC 14443 or NFC / ISO 15693 standard.
- the terminal T1 is for example a telecommunications terminal of the smartphone or other type.
- the smart card CD1 is configured in this example to communicate by contact with the external device T2 by means of the communication interface INT2.
- the INT2 interface includes, for example, external contacts to establish a contact connection, according to the ISO / IEC 7816 standard or other.
- the device T2 is for example a case (or equivalent) capable of at least partially accommodating the smart card in order to establish a connection by contact.
- at least one of the devices T1 and T2 comprises a fingerprint sensor (not shown) allowing the smart card CD1 to acquire fingerprints remotely.
- the smart card CD1 is able to collect a power supply received from the outside of the card.
- the devices T1 and T2 both constitute external power sources - respectively denoted SC1, SC2 - for the smart card CD1.
- the external devices T1 and T2 are respectively capable of supplying power supplies (or electrical energy) AL1, AL2 to the smart card CD1.
- the power supply AL1 is supplied in contactless mode to the smart card CD1 via the interface INT1 while the power supply AL2 is supplied by contact to the smart card CD1 via the interface INT2.
- the CD1 smart card and its application environment as shown in FIG. 1 constitute only one non-limiting example of implementation of the invention.
- certain elements of the CD1 smart card are described here only to facilitate understanding of the invention, these elements not being mandatory in order to implement the invention.
- Certain elements generally present in a smart card have been intentionally omitted because they are not necessary for the understanding of the present invention.
- the processor 2 controlled by the computer programs PG1 implements a certain number of modules, namely: a determination module MU2, an obtaining module MU4, a module MU6 analysis, a MU8 generation module, and possibly an MU10 authentication module.
- the MU2-MU8 modules are in particular configured to perform enrollment by fingerprint, as described in more detail later.
- the smart card CD1 uses an internal (SCO) or external (SC1 and / or SC2) power source to supply itself with electricity.
- the smart card CD1 can use at least two separate power supplies at the same time to carry out an enrollment.
- the sources together form a global power source (internal, external, or mixed internal / external).
- the determination module MU2 is configured to determine an applied mode from among a first mode MD1 and a second mode MD2.
- the term "applied mode” is understood to mean the mode (MD1 or MD2) in which the CD1 smart card is located during the execution of the enrollment.
- the power supply used by the smart card CD1 to perform the enrollment is below a predefined threshold ALm.
- the power supply used by the smart card CD1 to perform the enrollment is greater than or equal to the predefined threshold ALm.
- ALm can be set by a person skilled in the art on a case-by-case basis, depending in particular on the power requirements of the smart card CD1 to perform various processing operations during a fingerprint enrollment.
- the MU2 determination module is also configured to determine at least one PT parameter to be applied during enrollment.
- the obtaining module MU4 is configured to acquire N fingerprints FG1, for example by means of the fingerprint sensor 4 or from one of the external devices T1, T2 with which it is likely to cooperate. As described below, the fingerprints are acquired as image pixels representative of the fingerprints. In the example considered here, N is an integer greater than or equal to 2. Alternatively, N is an integer greater than or equal to 1.
- the MU6 analysis module is configured to perform an analysis of the N FG1 fingerprints acquired by the MU4 obtaining module. During this analysis, DT1 digital data representative of MT characteristic points of the FG1 fingerprints are extracted by reading the image pixels at a predetermined resolution level RL. As described below, the smart card CD1 can operate on this level of resolution RL in different ways depending on the case.
- the MU8 generation module is configured to generate an ML1 fingerprint model from at least the DT1 digital data extracted from the N first fingerprints.
- the generation module MU8 can generate this model ML1 by aggregating at least the digital data DT1 extracted by the analysis module MU6
- the generation module MU8 can thus record the fingerprint model ML1 in the non-volatile memory MR2 to allow subsequent authentication of a user by comparing at least one new fingerprint FG1 with the fingerprint model ML1. serving as a reference.
- the MU10 authentication module can be configured to authenticate an UR user by comparing a new FG1 fingerprint acquired by the MU4 obtaining module after enrollment, with the ML1 fingerprint template stored in the MR2 memory. If the new fingerprint matches the ML1 model, then the authentication has passed successfully. Otherwise, authentication fails or may possibly continue by verifying at least one other new FG1 fingerprint.
- the determination module MU2 is also configured to determine at least one PT parameter applied by the smart card CD1 during enrollment.
- the determination module MU2 is configured to determine as a function of the mode applied (MD1 or MD2) at least one parameter PT applied during the enrollment among the number N and the level of resolution RL, said at least one parameter being fixed. to be higher in the second mode MD2 than in the first mode MD1 so that the fingerprint model ML1 has a higher definition (or quality) in the second mode MD2 than in the first mode MD1.
- the smart card CD1 can operate on at least one of the PT parameters, namely the number N and the level of resolution RL, to vary the complexity of the enrollment and thus adapt the quality of the model.
- ML1 fingerprint according to the level of power supply available during enrollment.
- one of the parameter N and the level of resolution RL is adapted (and therefore varies) during enrollment as a function of the mode applied between MD1 and MD2, while the other parameter is fixed whatever or the MD1 / MD2 mode applied.
- the two parameters N and RL are adapted (and therefore vary) during enrollment as a function of the mode applied among MD1 and MD2.
- the adaptation of one or both parameters N and RL is done so that the definition (or quality) of the fingerprint model ML1 thus generated is greater if the card chip CD1 operates in the second mode MD2 (high power supply) only if it operates according to the first mode MD1 (lower power supply).
- This level of definition is representative of the resolution and / or the quantity of information stored in the ML1 fingerprint model, this information defining in particular MT characteristic points of a fingerprint.
- These characteristic MT points can in particular include minutiae characteristic of a fingerprint, as described below.
- the resolution level RL of the FG1 fingerprint reading during enrollment can be controlled and adapted in various ways.
- the resolution level RL is characterized for example by at least the number of neighboring pixels that the smart card CD1 takes into account to read a pixel of the image representing the fingerprint, for example to determine the gray level (or the color) of said pixel. This particular case and its variants are described below.
- MU2-MU10 modules of the smart card CD1 will appear more precisely in the exemplary embodiments described below with reference to FIGS. 3-7.
- the MU2-MU10 modules as shown in FIG. 2 only constitute a non-limiting example of implementation of the invention, other implementations being possible.
- the smart card CD1 as described above implements an enrollment method (or processing method) by executing the program. computer PG1. It is assumed that an authentication step S2 is performed by the smart card CD1 in order to authenticate a user UR wishing to enroll with the smart card CD1. This authentication S2 can be carried out in any way, for example by means of a verification of a secret PIN code or by verifying the validity of a fingerprint FG1 acquired by the smart card CD1.
- the smart card CD1 Upon detection that the S2 authentication has passed successfully, the smart card CD1 performs an S3 enrollment using an AL power supply supplied by an internal or external power source to the card at CD1.
- This S3 enrollment comprises steps S4 to S16 described below.
- the smart card CD1 has an internal power source SCO on board.
- this internal source SCO has a limited supply capacity, that is to say less than a threshold value ALm.
- the smart card CD1 is able to cooperate with the external device T1 and / or T2 in order in particular to collect the power supply AL1 and / or AL2 supplied respectively by these devices.
- the smart card CD1 determines the MD mode applied (that is to say the MD mode in which the smart card CD1 is located or operates) during the enrollment S3 among MD1 and MD2 modes. As already indicated, if the power supply (or electrical energy) AL collected by the smart card CD1 during enrollment S3 is less than a predefined threshold ALm, it then operates according to the first mode MD1 (low power supply). If, on the other hand, the power supply AL collected by the smart card CD1 during enrollment S3 is greater than or equal to this predefined threshold ALm, it then operates according to the second mode MD2 (high power supply).
- the power supply AL collected by the smart card CD1 during enrollment S3 is greater than or equal to this predefined threshold ALm, it then operates according to the second mode MD2 (high power supply).
- the threshold value ALm which in the present case defines the type of a power source, namely whether it is a source providing a so-called “low” power supply ( MD1) or a source providing a so-called “strong” power supply (MD2).
- This threshold value ALm can be defined in terms of power delivered for example (or voltage, or current), and can be adapted by those skilled in the art according in particular to the needs in terms of power supply of the smart card CD1 , particularly in view of the processing likely to be carried out by the card during enrollment.
- the power supply AL collected by the smart card CD1 during enrollment S3 only comes from a single source SCO, SC1 or SC2, although other implementations are possible where the smart card CD1 can simultaneously collect the power supply delivered by a plurality of power supply sources.
- the smart card CD1 is coupled with the device T1 or T2, it receives the corresponding electrical power supply AL1 or AL2 so that it does not use its internal source SCO for power.
- other implementations are possible.
- the sources SCO and SC1 constitute "weak” power sources while the source SC2 constitutes a "strong” power source.
- the power supplies ALO and AL1 delivered by the sources SCO and SC1 are less than the predefined threshold ALm, while the power supply AL2 delivered by the source SC2 is greater than or equal to the predefined threshold ALm.
- the smartphone T1 is configured to supply a limited power supply AL1 in contactless mode (even though it has a more substantial power source than the smart card CD1) while the case T2 is on board here.
- a power source SC2 capable of supplying a large power supply AL2 to the smart card CD1 when these two elements cooperate by contact.
- other examples are possible.
- the smart card CD1 can in particular determine at least one parameter characterizing (directly or indirectly) the power source that it uses and applies predefined rules RL1 stored in its memory MR2 to deduce therefrom whether it is the MD1 mode or MD2 which is applicable.
- the smart card CD1 receives a signal SG identifying an external device (T1 or T2) with which it cooperates during the enrollment S3.
- the smart card CD1 receives in this example, via its communication interface INT1 or INT2, the signal SG1 or SG2 respectively identifying the external devices T1 and T2.
- the smart card CD1 determines (S4), from the received SG signal, the applied MD mode (either MD1 or MD2) during S3 enrollment by applying the RL1 rules.
- the applied MD mode either MD1 or MD2
- the predefined rules RL1 specify that in response to the signal SG1 (respectively SG2), the smart card CD1 detects that it is receiving an electrical power supply AL lower (respectively greater than or equal) to the predefined threshold ALm and deduces that it operates according to the first mode MD1 (respectively the second mode MD2). It is therefore considered in this particular example that the smart card CD1 is preconfigured to determine from the external device with which it cooperates whether the corresponding electric power supply reaches or not the predefined threshold ALm.
- the smart card CD1 determines (S4) in accordance with the rules RL1 that it operates in the first MD1 (low power supply) mode, assuming the internal power source SCO is used.
- the signal SG received from an external device identifies the power supply (SC1 or SC2) supplied to the smart card CD1 during enrollment S3. From the predefined rules RL1, the smart card CD1 can thus determine which mode (MD1 or MD2) to apply as a function of the signal SG received.
- the signal SG includes any information allowing the smart card CD1 to determine which mode (MD1 or MD2) to apply during S3 enrollment.
- the signal SG received from an external device (T1 or T2) identifies the mode to be applied (MD1 or MD2), so that no predefined rule RL1 is necessary.
- the smart card CD1 determines in S4 the mode applied (MD1 or MD2) during the enrollment S3 as a function of the use of the communication interface (s) INT that the card contains. chip (namely the INT1 and INT2 communication interfaces in this case).
- the smart card CD1 determines whether one of its communication interfaces INT1 and INT2 is used during enrollment S3 (to cooperate with one of the external devices T1 and T2 in this example) and, if so, which of these communication interfaces is used.
- processor 2 determines for example if there is an activity (signals, power supply etc.) on each of the communication interfaces INT 1, INT2.
- the smart card CD1 can use any one of the communication interfaces I NT 1, INT2 during enrollment S3 or, alternatively, does not use either of the two communication interfaces I NT 1, INT2 for perform S3 enrollment. From the use or not of its communication interfaces INT 1, INT2, the smart card CD1 thus determines which of the modes MD1 and MD2 is applied during enrollment S3.
- the predefined rules RL1 therefore specify that on detection that the first communication interface INT1 (respectively the second communication interface INT2) is used during enrollment S3, the smart card CD1 selects the first mode MD1 (respectively the second mode MD2) in S4.
- the smart card CD1 selects the first MD1 mode in accordance with the predefined rules RL1. If the smart card CD1 is connected via one of its communication interfaces INT1, INT2 with an external device, this presupposes that it receives a respective power supply AL1, AL2 from said communication interface.
- the smart card CD1 is configured to use its own internal SCO power supply. It is therefore considered in this particular example that the smart card CD1 is preconfigured to determine, from its use (or not) of its communication interfaces, whether or not the received power supply reaches the predefined threshold ALm.
- the smart card CD1 evaluates an electrical characteristic (power, voltage, current, etc.) of the power supply AL that it receives (or detects) in from an internal or external power source.
- the smart card CD1 compares the level of power supply detected from this characteristic, with the predefined threshold value ALm, and determines the mode applied (MD1 or MD2) during enrollment S3 according to the result of this comparison .
- the smart card CD1 therefore determines that it is operating in the first mode MD1 if the power supply AL received is less than the predefined threshold ALm and operates in the second mode MD2 if the power supply AL is greater than or equal to said predefined threshold ALm.
- the smart card CD1 determines (S6) at least one PT parameter that it must apply during the enrollment S3.
- parameter to apply is meant a parameter that is used by the CD1 smart card to perform S3 enrollment.
- said at least one PT parameter comprises at least one of the number N applied in S8 and the resolution level RL applied in S10.
- this step S6 it thus adapts this or these PT parameters as a function of the MD1 or MD2 mode in which the smart card CD1 operates to carry out the enrollment S3.
- the CD1 smart card operates in the first MD1 mode (limited power supply), it is necessary to limit the complexity and processing time of the S3 enrollment.
- the smart card CD1 has a larger power supply and can therefore advantageously adapt its PT parameters in order to increase the complexity and the processing time of the enrollment.
- the determination step S6 also called the adaptation step and its consequences are described in more detail later.
- the smart card CD1 acquires N fingerprints FG1, N being an integer greater than or equal to 2. These fingerprints FG1 obtained in S8 constitute “first” prints. digital within the meaning of the invention.
- each fingerprint is acquired in S8 in the form of an image, namely an arrangement of image pixels PX, these pixels being representative of the FG1 fingerprint in question.
- Each pixel PX is characterized in particular by a gray level (or a color).
- the number N of fingerprints FG1 acquired by the smart card CD1 in S8 can be adapted according to the mode MD1 or MD2 which is applied.
- the smart card CD1 can apply in S8 a number N which is greater in the second mode MD2 than in the first mode MD1 to take account of the fact that the smart card CD1 has more energy in MD2 mode and therefore can process more FG1 fingerprints during S3 enrollment.
- the smart card CD1 sends to an external terminal a message comprising the number N to be applied in S8, in order to enable the terminal external to invite the UR user to perform the requested fingerprint acquisitions.
- This external terminal can be for example the external device (T1 or T2) with which the smart card CD1 is coupled or any other terminal.
- the smart card CD1 performs in S10 an analysis of the N fingerprints FG1 acquired. During this analysis, digital data DT1 representative of characteristic points MT of the N fingerprints FG1 are extracted by reading the image pixels PX at a predetermined resolution level RL.
- the S10 scan can start after completion of the acquisition step S8 or, alternatively, the S10 scan can run while the S8 acquisition is still in progress.
- each FG1 fingerprint constitutes a representation of the dermo-epidermal ridges (or papillary ridges) of a finger of an UR user.
- the geometry of these ridges forms a pattern that is unique to each person and allows it to be authenticated with great reliability.
- This pattern is characterized by characteristic points (also called singular points) denoted MT (figure 5).
- the CD1 smart card thus reads all or part of the PX image pixels of the FG1 fingerprints acquired so as to extract the DT1 digital data mentioned above ( Figure 4).
- These digital data DT1 define characteristic points MT of each imprint FG1.
- these MT characteristic points include in particular minutiae (local singular points), namely points of irregularity located on the papillary lines (terminations, bifurcations, islands, etc.). Other MV characteristic points can however be taken into account (global singular points).
- the smart card CD1 selects (S12) all or part of the image pixels PX of each fingerprint FG1.
- the pixels selected by the smart card CD1 in S12 are denoted PX1 (FIG. 5).
- the smart card CD1 then evaluates (S14) the gray level (or the color) characterizing respectively each pixel PX1 selected in S12 so as to locate predetermined characteristic points MT in said fingerprint FG1 concerned.
- the smart card CD1 can play on various factors in S6 to adapt the level (or degree) of resolution RL applied when reading S14 during analysis S10.
- the smart card CD1 During a generation step S16 (FIG. 4), the smart card CD1 generates a fingerprint model ML1 from at least the digital data DT1 obtained in S10. In the present case, the smart card CD1 generates (S16) the fingerprint model ML1 by aggregating the digital data DT1 extracted in S10 from the N fingerprints FG1. As described later, other data can also be taken into account to generate the ML1 model. How the CD1 smart card processes the DT1 digital data to generate the ML1 fingerprint template is left to the discretion of those skilled in the art, with various implementations possible.
- the S16 generation comes down to creating a new ML1 fingerprint model from the DT1 digital data extracted in S10.
- the DT1 fingerprint data of the N FG1 fingerprints are aggregated together to form the new model of the ML1 fingerprint.
- the fingerprint model ML1 is generated from the digital data DT1 extracted in S10 and from a pre-existing fingerprint model ML, that is to say say a model of fingerprint ML (for example ML2 described below) pre-recorded in the memory MR2.
- generation S16 therefore amounts to updating a pre-existing fingerprint model to generate the new model ML1.
- the digital data DT1 of each fingerprint FG1 obtained in S8 are aggregated (S16), fingerprint by fingerprint, with (or in) the prerecorded fingerprint model ML so as to obtain the updated ML1 fingerprint template.
- the fingerprint model ML1 thus generated then replaces the pre-existing fingerprint model.
- the fingerprint model ML1 generated in S16 serves as a reference print to subsequently determine whether a fingerprint acquired during a subsequent authentication is valid or not.
- the ML1 model is created so as to include information characterizing as faithfully as possible the anatomy of a finger of the UR user concerned.
- the ML1 model comprises in particular digital data DT1 representative of characteristic points MT, as already described.
- the smart card CD1 records the fingerprint model ML1 in its memory MR2 to allow the subsequent authentication of fingerprints FG1 by comparison with the fingerprint model ML1 serving as reference.
- the smart card CD1 can perform an authentication step S18 based on the fingerprint model ML1 stored in memory.
- the CD1 smart card acquires one (or more) new FG1 fingerprint by means of its fingerprint sensor 4 or an external device with which it cooperates.
- the CD1 smart card compares this new FG1 fingerprint with the ML fingerprint model.
- the smart card CD1 detects that the authentication S18 has passed successfully only if this fingerprint FG1 coincides with the model ML1. Otherwise, S18 authentication fails or S18 authentication continues based on another FG1 fingerprint acquisition.
- the smart card CD1 determines at least one parameter PT to be applied during the enrollment S3, namely at least one among the number N of fingerprint acquisition applied in S8 and the level of resolution RL characterizing the reading during the analysis S10 of the image pixels PX of the fingerprints FG1 in order to extract the finger data DT1 therefrom.
- At least one parameter PT among the number N and the level of resolution RL is determined (or adapted) in S6 according to the mode applied (MD1 or MD2) during the enrollment (as detected in S6) .
- said at least one parameter is set to be higher in the second mode MD2 than in the first mode MD1 so that the fingerprint model ML1 generated in S16 has a higher definition (or level of definition, or quality) when the smart card CD1 operates in the second mode MD2 than when the smart card CD1 operates according to the first mode MD1 during enrollment S3.
- the definition (or quality) of the fingerprint model ML1 is representative of the resolution and / or the quantity of information stored in this model.
- the definition of the fingerprint model ML1 is for example characterized by the number of characteristic points MT (of minutiae for example) defined in the model ML, these points allowing a comparison with corresponding regions of a fingerprint to be verified.
- the definition of the fingerprint model ML1 can also be characterized by the quantity or precision of the information characterizing each characteristic point MT, as described below.
- the smart card CD1 adapts the number N in S6 as a function of the MD1 / MD2 mode determined in S4.
- the RL resolution level can be kept constant regardless of the MD1 / MD2 mode applied.
- the number N is adapted so that it is higher in the MD2 mode than in the MD1 mode.
- the smart card CD1 is configured to require 8 acquisitions of fingerprint FG1 in the second mode MD2 and only 5 acquisition of fingerprint FG1 in the first mode MD1, which makes it possible to accelerate the enrollment and limit the consumption of resources and energy when the smart card CD1 has only a limited power source (MD1 mode).
- MD1 mode a limited power source
- adapting the number N increases the number of FG1 fingerprints used in the second MD2 mode to generate the ML fingerprint template, which leads to increased quality of said template.
- the smart card CD1 adapts the resolution level RL in S6 as a function of the MD1 / MD2 mode determined in S4.
- the number N can be kept constant N regardless of the MD1 / MD2 mode applied.
- the level of resolution RL is adapted so that it is higher in the second mode MD2 than in the first mode MD1. The nature of this RL resolution level and how it can be controlled are described below.
- the smart card CD1 adapts in S6 the number N and the level of resolution RL so that they are higher in the second mode MD2 than in the first mode MD1.
- the RL resolution level of this reading is a function of (or includes) various factors - known as resolution factors - on which the CD1 smart card can play singly or in combination to increase or decrease the RL resolution level depending on the mode. MD1 / MD2 applied. Examples of embodiments illustrating the control of some of these resolution factors are described below.
- the smart card CD1 can select (S12) for each fingerprint FG1 pixels PX1 from among the totality of pixels PX forming said fingerprint FG1. This selection can include all or part of the PX image pixels of each FG1 fingerprint.
- the smart card CD1 evaluates in S14 a gray level (or a color) characterizing each pixel PX1 selected in S12. This evaluation is carried out from a reading of each selected pixel PX1 and from a reading of X neighboring pixels - denoted PX2 - of said selected pixel PX1.
- the smart card CD1 reads (or analyzes) this pixel as well as X neighboring PX2 pixels (for example X predetermined neighboring pixels adjacent to said pixel PX1) and combines the gray levels (or color) obtained for all these pixels in order to deduce therefrom the gray level (or color) of the selected pixel PX1.
- the number X is an integer greater than or equal to 0 (in a particular example, X> 1).
- the smart card DV1 adapts in S6 (FIG. 4) this number X so as to be higher in the second mode MD2 than in the first mode MD1, which has the consequence of increasing the definition (or quality) of the ML1 fingerprint model obtained in the second MD2 mode compared to the first MD1 mode.
- This number X characterizes the level of resolution RL of the reading of image pixels PX carried out in S10.
- the smart card CD1 can generate a gray level (or a color) representative of each pixel PX1 selected in S12.
- the smart card CD1 generates for example for each pixel PX1 selected a respective gray level (or color) encoded according to a predetermined encoding level denoted Y.
- this level of encoding Y is adapted by the smart card CD1 in S6 (FIG. 4) so as to be higher in the second mode MD2 than in the first mode MD1, which results in to increase the definition (or quality) of the fingerprint model ML1 obtained in the second mode MD2 compared to the first mode MD1.
- This Y factor characterizes the RL resolution level of the PX image pixel reading performed in S10.
- the algorithm implemented by the smart card CD1 to read the color of the image pixels PX can thus be appropriately configured by adapting the resolution of the color coding according to the MD1 / MD2 mode applied during enrollment. S3.
- the smart card CD1 defines (or adapts) in S6 the number (or proportion) of pixels PX1 selected in S12 to read the gray levels.
- the smart card CD1 can adapt the number of pixels PX1 selected so that it is higher in the second mode MD2 than in the first mode MD1.
- the higher the number of pixels PX1 selected in S12 the higher the level of resolution RL of the reading S14 (and therefore of the digital data DT1 thus extracted).
- this number of pixels PX1 per fingerprint FG1 characterizes the level of resolution RL of the reading of image pixels PX carried out in S10.
- the smart card CD1 performs the acquisition S8 and the analysis S10 of the N fingerprints FG1 in a predetermined time range, this time range being adapted by the chip card CD1 during the enrollment S3 so as to be longer in the second mode MD2 than in the first mode MD1.
- the smart card CD1 advantageously has the time allocated / allocated to carry out the enrollment S3 according to the level of resolution RL chosen.
- the present invention advantageously enables the CD1 smart card to reliably and efficiently authenticate a user by means of fingerprints. Thanks to the invention, the CD1 smart card can perform fingerprint authentication optimally despite the varying contexts in which a user and the smart card are likely to find themselves.
- generating a high definition fingerprint model requires time and processing resources that are not always available for a smart card.
- the available resources are intrinsically linked to the power supply available to the smart card to complete the enrollment. Due to the various internal and external power sources that a smart card can use as appropriate, it is necessary to adapt the way in which a fingerprint enrollment is performed according to the context in which the smart card. is located.
- the invention makes it possible to generate a fingerprint model that is as rich and complete as possible, within the limits imposed by the power supply resources. electrical (and therefore in processing) available to the smart card to perform the enrollment.
- a user can for example achieve a more faithful enrollment when his smart card is coupled by contact with a case (having a large battery) than when his smart card is coupled in contactlessly with a smartphone (configured to provide a power supply). limited electric).
- the smart card of the invention is thus capable of generating fingerprint models that are faithful to the users, while optimizing the use of the resources of the smart card.
- the smart card adapts the enrollment setting according to the level of the available power source.
- the number N of fingerprints used and / or the resolution level RL for reading these fingerprints can be adapted to guarantee a higher definition of the fingerprint image model in the second mode.
- MD2 than in the first MD1 mode.
- various resolution factors described above can be adapted (either one or several at the same time).
- the smart card CD1 determines a level of recovery of each first fingerprint FG1 obtained in S8 with respect to a another first fingerprint FG1 obtained in S8 or vis-à-vis a pre-existing fingerprint model from which the fingerprint model ML1 is generated during generation S16. More particularly, in the particular case where the S3 enrollment does not aim to update a pre-existing fingerprint model (that is to say when it comes to generating a new model), then the smart card CD1 determines a level of recovery of each first fingerprint FG1 obtained in S8 with respect to another first fingerprint FG1 obtained in S8 (for example with respect to the fingerprint FG1 acquired first during of acquisition S8).
- the smart card CD1 determines a level of recovery of each first FG1 fingerprint obtained in S8 with respect to the pre-existing fingerprint model.
- FIG. 6 illustrates for example an overlap zone Z1 between 2 fingerprints FG1a and FG1b and a predefined fingerprint model ML2 corresponding to the same finger.
- An overlap level characterizes the degree of similarity of the fingerprints to each other or to a fingerprint model ML, this degree being a function of the extent of the overlap zone Z1 considered.
- the smart card CD1 then verifies, for each first fingerprint FG1 obtained during enrollment S3, whether the determined level of recovery reaches a predetermined minimum level of recovery TH1.
- the fingerprint model ML1 is then generated from the digital data DT1 extracted from each first fingerprint FG1 for which the associated level of coverage reaches the predetermined minimum level of coverage. TH1.
- the ML1 model generated in S16 does not take into account each FG1 fingerprint that does not reach the required minimum TH1 level of overlap.
- Checking the level of recovery allows the CD1 smart card to check whether it is able to aggregate the different fingerprints together to form an ML1 fingerprint template.
- the common part of the fingerprints is used during the generation of the ML fingerprint template to verify that each acquired fingerprint is a fingerprint of the same finger (possibly of the same finger as that of the pre-existing fingerprint template) and to precisely aggregating the digital data extracted from each fingerprint FG1 together and / or with a pre-existing fingerprint template (FIG. 4).
- the smart card CD1 adapts the minimum recovery level TH1 during the first enrollment S3, so as to be higher if no initial enrollment (other than enrollment S3) has not been carried out prior to the first enrollment.
- 'S3 enrollment Figure 4
- the S3 enrollment can, depending on the case, be the very first enrollment (initial enrollment) carried out during the initial configuration of the smart card CD1 or, alternatively, be a subsequent enrollment (for example an update after a time of 'given use).
- the smart card CD1 can consult its memory to determine whether it already contains a fingerprint model. If not, this means that no prior enrollment has been carried out from the point of view of the smart card CD1.
- the CD1 smart card can thus play on the required TH1 overlap level between the FG1 fingerprints acquired during S3 enrollment to authorize the aggregation of fingerprints, depending on the type of enrollment considered.
- the smart card CD1 can therefore advantageously adapt the minimum recovery level TH1 during the first enrollment S3, so as to be higher if the enrollment S3 considered is an initial enrollment (no other enrollment has been carried out beforehand) than if the S3 enrollment considered is a subsequent enrollment (update subsequent to the initial enrollment).
- a higher TH1 minimum overlap level may be required in order to obtain a good quality initial impression model.
- the required level of coverage may be lower as anatomical changes may have affected the user's fingers and / or changes in the sensor may also have occurred.
- a preliminary enrollment (or initial enrollment) S30 is executed by the smart card CD1 before the enrollment S3 described above ( figure 4).
- an initial fingerprint model ML2 is generated and stored in the memory MR2.
- This model ML2 can be generated in any way from one or a plurality of fingerprints acquired by the smart card CD1.
- the preliminary enrollment S30 is carried out in a manner analogous to the enrollment S3.
- the smart card CD1 performs the authentication S2 (FIG. 4) to authenticate a user UR prior to the enrollment S3.
- the enrollment S3 is therefore not a preliminary enrollment but a new enrollment, which aims for example to update the initial model of fingerprint ML2.
- the smart card CD1 acquires (S34, FIG. 7) at least one fingerprint denoted FG2, by means of the fingerprint sensor 4 or by using any external device carrying a such sensor. These FG2 fingerprints acquired in S34 constitute "second" fingerprints within the meaning of the invention.
- the smart card CD1 compares in S36 said at least one second fingerprint FG2, or data obtained from said at least one second fingerprint FG2, with the initial fingerprint model ML2 pre-recorded in the memory MR2 of the card CD1 chip during preliminary enrollment S30.
- the smart card CD1 determines in S38 that the authentication S2 has successfully passed from the result of the comparison S36. More precisely, if said at least one second fingerprint FG2 (or the data obtained from it (s)) coincides with the initial model ML2, the authentication S2 is passed successfully. Otherwise, authentication fails or may possibly continue from at least one new FG2 fingerprint acquisition.
- the previously described S3 enrollment is then executed by the CD1 smart card upon detection that the S2 authentication has passed successfully.
- the fingerprint model ML1 is generated in S16 (FIG. 4) from the fingerprints FG1 obtained in S8.
- the first fingerprints FG1 obtained in S8 comprise at least one fingerprint selected from among the second fingerprint (s) FG2 acquired during the authentication S2 preceding the enrollment S3.
- the obtaining step S8 comprises the selection, as the first fingerprint FG1, of at least one second fingerprint FG2 acquired during the prior authentication S2. This selection is for example carried out so as to select only the second fingerprint (s) FG2 which have a predetermined minimum level of overlap (or a level of coincidence) with the predefined fingerprint model. ML2.
- the smart card CD1 can, in addition to the selection of at least one second fingerprint FG2 as first fingerprints FG1, acquire one or more other new fingerprints by as the first fingerprints FG1, in order to reach the number N of first fingerprints FG1 which it is necessary to obtain in order to carry out the analysis step S10 (FIG. 4).
- the smart card CD1 can obtain in S8 the N first fingerprints FG1 by selecting at least one fingerprint FG2 already acquired during the authentication S2 preceding the enrollment S3 and / or by acquisition of at least one. new FG1 fingerprint upon S3 enrollment.
- the use of all or part of the FG2 fingerprints acquired during the S2 authentication to generate in S16 (FIG. 4) the ML1 fingerprint model makes it possible to significantly accelerate the S3 enrollment and to enrich the model of ML1 fingerprint while limiting the necessary processing and time resources.
- the use of the fingerprints FG2 acquired during the authentication S2 to generate the model ML1 can be done transparently for the user.
- the smart card CD1 selects only 6 fingerprints among the 8, upon detection that only these 6 fingerprints reach a predetermined minimum level of overlap. with the pre-existing ML2 fingerprint model.
- the fingerprint model ML1 is generated in S16 (FIG. 4) from the digital data DT1 extracted in S10 and from the fingerprint model ML2 pre-recorded in the MR2 memory.
- Generation S16 therefore amounts to updating a pre-existing fingerprint model to generate the new ML1 fingerprint model.
- the digital data DT1 of each first imprint FG1 obtained in S8 are aggregated (S16), imprint by imprint, with (or in) the pre-recorded imprint model ML2 so as to obtain the updated fingerprint model ML1.
- the fingerprint model ML1 thus generated then replaces the pre-existing fingerprint model ML2.
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Abstract
Description
Claims
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| FR1915263A FR3105510B1 (fr) | 2019-12-20 | 2019-12-20 | Enrôlement par empreinte digitale sur une carte à puce |
| PCT/FR2020/052561 WO2021123686A1 (fr) | 2019-12-20 | 2020-12-18 | Enrolement par empreinte digitale sur une carte a puce |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| EP4078437A1 true EP4078437A1 (fr) | 2022-10-26 |
Family
ID=70738644
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| EP20851214.5A Pending EP4078437A1 (fr) | 2019-12-20 | 2020-12-18 | Enrolement par empreinte digitale sur une carte a puce |
Country Status (5)
| Country | Link |
|---|---|
| US (1) | US20230017744A1 (fr) |
| EP (1) | EP4078437A1 (fr) |
| KR (1) | KR20220113990A (fr) |
| FR (1) | FR3105510B1 (fr) |
| WO (1) | WO2021123686A1 (fr) |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE60215810T2 (de) * | 2001-06-27 | 2007-09-06 | Activcard Ireland Ltd. | Verfahren und gerät zur transformation eines bildes einer biologischen oberfläche |
| GB2507539A (en) * | 2012-11-02 | 2014-05-07 | Zwipe As | Matching sets of minutiae using local neighbourhoods |
| KR20150113152A (ko) * | 2013-01-29 | 2015-10-07 | 메리 그레이스 | 개선된 보안 특징을 갖는 스마트 카드 및 스마트 카드 시스템 |
| US9261991B2 (en) * | 2013-05-28 | 2016-02-16 | Google Technology Holdings LLC | Multi-layered sensing with multiple resolutions |
| GB201611308D0 (en) * | 2016-06-29 | 2016-08-10 | Zwipe As | Biometrically authorisable device |
| SE1750172A1 (sv) * | 2017-02-20 | 2018-08-21 | Fingerprint Cards Ab | Method and smart card adapted for progressive fingerprint enrollment |
-
2019
- 2019-12-20 FR FR1915263A patent/FR3105510B1/fr active Active
-
2020
- 2020-12-18 KR KR1020227023034A patent/KR20220113990A/ko active Pending
- 2020-12-18 US US17/786,691 patent/US20230017744A1/en not_active Abandoned
- 2020-12-18 WO PCT/FR2020/052561 patent/WO2021123686A1/fr not_active Ceased
- 2020-12-18 EP EP20851214.5A patent/EP4078437A1/fr active Pending
Also Published As
| Publication number | Publication date |
|---|---|
| US20230017744A1 (en) | 2023-01-19 |
| FR3105510B1 (fr) | 2022-02-11 |
| FR3105510A1 (fr) | 2021-06-25 |
| KR20220113990A (ko) | 2022-08-17 |
| WO2021123686A1 (fr) | 2021-06-24 |
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