Method for recognizing an individual by merging at least two biometric measurement results, central server, corresponding computer program product
1. Field of the invention
The field of the invention is that of multimodal biometric recognition.
Multimodal biometry is understood to mean a recognition technique that takes into account several biometric modalities (that is to say, several biometric measurement results), for example of the fingerprint type, the iris imprint, the shape of the hand, the recognition of the speaker ...
Such a fusion recognition technique makes it possible in particular to combine these different results to identify and / or authenticate an individual.
Thus, the invention relates to a technique for recognizing individuals by fusion of biometric measurement results.
It finds particular applications in fields such as the security or customization of multimedia services, and allows for example to allow or not access to files, services, buildings ..., to certain individuals identified by biometric data.
2. Prior Art
Numerous technologies based on biometrics are known to date allowing the recognition of individuals, in particular with a view to their identification or authentication. These technologies are conventionally based on the use of a single type of biometric modality (the fingerprint, the voice, the iris or the face for example).
The recognition of an individual comprises two phases: a first phase of enrollment, also called learning phase, and a second phase of identification / authentication.
The first phase consists of recording different reference biometric measurements corresponding to each individual likely to use the recognition system. These individuals are called in the following description "users" belonging to a "group" to which access is allowed. These reference biometric measurements acquired in the learning phase are then used in the second phase of identification / authentication of the user of the system, to be compared to a current measurement, according to specific processes. These biometric recognition systems based conventionally on the use of a single type of biometric modality have certain disadvantages, both in terms of their recognition performance and their use constraints.
In an attempt to overcome these disadvantages, techniques based on the combination of several biometric recognition methods have been proposed.
Thus, according to the prior art, approaches exist that propose to combine the results of two measurements from different biometric sensors. These approaches are mainly based on probabilistic methods. For example, EP-1274047 "Multimodal
Biometry ", proposes a multimodal biometric authentication technique of at least one individual, using two statistically combined biometrically similar measures of resemblance to assign the individual to recognize to one of several classes.
Biometric resemblance measures are measures of resemblance between biometric data determined for the individual and reference data for individuals of different classes. These measures of resemblance are, according to this document, distance calculations, or calculations of scores for example. A statistical density probability method is then used to combine these similarity measures. A disadvantage of this technique of the prior art is that it relies on a statistical method that is not configurable according to the environment. This technique is therefore not optimized according to the environment of use.
Another disadvantage of this technique of the prior art is its low generality.
Indeed, this technique requires a relearning of the entire system to take into account a new biometric measurement. 3. Presentation of the invention
The invention proposes a novel solution that does not have all of these disadvantages of the prior art, in the form of a method of recognizing an individual by merging at least two biometric measurement results, which comprises a first melting step delivering a first recognition result.
According to the invention, such a method also comprises:
at least one second melting step, delivering a second recognition result, a step of selecting a recognition mode, said selected mode, from at least three recognition modes, corresponding on the one hand to each of said melting steps taken independently, and on the other hand to at least a combination of at least two of said melting steps, and a recognition decision step, delivering a determined recognition decision based on said one or more recognition results of said selected mode.
Thus, the invention is based on a new and inventive approach to the recognition of an individual by merging several results of biometric measurements.
More precisely, the different merge steps each take into account several results of biometric measurements,
and allow to give a recognition result combining several biometric modalities, so as to improve the recognition performance.
The step of selecting a recognition mode makes it possible, among other things, to choose an optimal combination of the different fusion results, depending on the environment and / or the desired application. Thus, this selection step solves the problem of the genericity of the biometric recognition systems of an individual by taking into account variable parameters. Depending on the case and need, one or more merger results are taken into consideration in the decision step to make an individual recognition decision. The technique of the invention thus allows, for example, an implementation adapted to several applications.
According to one particular characteristic of the invention, said selection step takes account of at least one item of information belonging to the group comprising: a level of security, - a type of biometric measurements, - a quality level of at least one biometric measurement , a quality level of at least one of the recognition results, a maximum execution time.
A recognition mode is thus selected taking into account at least one piece of information representative of the parameters of the environment or application desired.
This information thus makes it possible to configure a recognition system, in particular by adapting it to a desired level of security. For example, some more restrictive merging methods than others may be chosen for self-recognition applications requiring a high level of security.
Likewise, certain fusion methods appear more reliable for certain types of biometric measurements and will be chosen particularly in the case where these types of biometric measurements are used in recognition.
The environments or the acquisition conditions of the biometric measurements can also be taken into account for the selection of a recognition mode, thus enabling the recognition system to adapt, for example, to the noise level for the voice biometric measurement, for example. Brightness conditions for facial biometric measurement ...
This selection step may also take into consideration a desired quality level for one or more recognition results and / or a desired maximum execution time and select the optimal recognition mode accordingly.
According to a particular embodiment,
the first and / or second melting steps belong to the group comprising: - distance mergers,
- mergers of opinion.
For example, the first merge step is of distance merge type, the second merge step is of the opinion merge type, and the three possible recognition modes are based on: - either a distance merge or a merge opinions, either on a combination of distance mergers and opinions. The distance merging corresponds in particular to a combination of ordered lists of the distances of each member of the user group with respect to the best biometric measurement result. At this best result, we assign a distance equal to zero, the second best result we assign a distance equal to 1 and so on.
The calculation time of this merge is quite fast.
Merger of opinions is a combination of the results of weighted biometric measurements. The weighting rule used is configurable for example according to the desired level of security and / or ergonomics.
Merger of opinions requires a more expensive calculation than distance merging, but typically has a more accurate merging result.
In particular, at least one of the merging steps can implement predetermined weighting coefficients. These weighting coefficients are for example determined during a manual preliminary calibration phase (trial / error), or during an automatic learning phase.
It can notably be noted that the values of the weighting coefficients depend on the quality of the biometric measurements: for example a saturation level, a signal-to-noise ratio, a contrast level of an image, etc. According to a particular aspect of the In the invention, the selected mode implements a merge rule corresponding to a sum or multiplication of at least two biometric measurement results, determined according to a desired security level. Thus, if the desired security level is low, the merge rule implemented corresponds to a sum of the results of biometric measurements, since the rule of the sum is much more permissive than the rule of the product. Indeed, a low probability (less than 0.1 for example) of a biometric measurement result does not penalize the overall result.
For the product rule, however, it suffices that one of the probabilities of one of the results of biometric measurements is very small (less than 0.1 for example), or even lower than the rest of the results, so that the result overall merger is very low, even negligible, and therefore that an individual is not recognized. An example of opinion fusion results, applied to two biometric measurement results, using two different weighting rules is presented in the description of an embodiment of the invention (section 5.4.4).
According to a particular embodiment, the recognition method according to the invention is implemented in a central server, the results of biometric measurements from at least two remote biometric servers.
Thus, according to this particular embodiment, the central server receives requests for recognition from client services, as well as biometric measurements made, and for example transmits them in the form of files to the remote biometric servers, which process them and send back the results of biometric measurements at the central server. A database may also be available in this central server to keep a history of all the recognition requests made.
According to this particular embodiment, the recognition decision is transmitted to at least one remote client server.
The remote client server manages the recognition requests, records the biometric measurements, transmits them to the central server and expects from the central server the recognition decision enabling it to respond to the recognition request.
In particular, the central server advantageously communicates with the biometric servers and the client server according to an XML formalism (in English "Extensible Markup Language").
In particular, the use of XML facilitates the genericity of the system and facilitates the addition of new biometric modalities. Thus, the input / output parameters of the central server follow the XML formalism.
In addition, exchanges of biometric measurements and recognition results are done via specific XML requests: a specific XML request for the sending of the biometric measurements by the client server to the central server, a specific XML request for the transmission of these measurements. biometrically by the central server to the biometric servers, a specific XML request for the transmission of biometric measurement results by the biometric servers to the central server, a specific XML request for sending the recognition decision by the central server to the client server. ..
In particular, the central server simultaneously accesses all the remote biometric servers.
The biometric measurements transmitted by the client servers to the central server are transferred, via specific XML requests, simultaneously to the corresponding biometric servers.
We thus gain in processing time since all the results are available simultaneously.
Another aspect of the invention relates to a central server comprising first merging means delivering a first recognition result.
According to the invention, such a central server also comprises: second merging means, delivering at least a second recognition result, means for selecting a recognition mode, selecting a recognition mode from among at least three recognition modes corresponding on the one hand to the implementation of each of the merging means taken independently, and on the other hand to the implementation of at least a combination of at least two of the merging means, said selected mode, and recognition decision means, delivering a recognition decision determined according to the recognition result (s) of the selected mode.
Such a central server is particularly adapted to implement the method of recognition by fusion of biometric measurements described above.
Another aspect of the invention relates to a computer program product downloadable from a communication network and / or recorded on a computer readable medium and / or executable by a processor, comprising program code instructions for the implementation method of recognizing an individual by fusing at least two biometric measurement results described above. 4.
List of Figures
Other features and advantages of the invention will appear more clearly on reading the following description of a particular embodiment, given as a simple illustrative and nonlimiting example, and the appended drawings, among which:
- Figure 1 shows the steps of the method of recognizing an individual according to a particular embodiment of the invention; FIG. 2A shows an example of a functional architecture of the recognition system according to a particular embodiment of the invention;
FIG. 2B describes a data flow sequence of a central server according to a particular embodiment of the invention; Figure 3 shows an example of a central server; FIG. 4 illustrates an overall view illustrating a fusion mechanism according to the invention;
FIG. 5 presents an exemplary implementation of a fusion algorithm according to the invention; FIG. 6 illustrates an exemplary structure of a portion of the central server implementing the method of recognizing an individual according to a particular embodiment of the invention.
5. Description of an embodiment of the invention 5.7 General principle
As illustrated in relation with FIG. 1, the general principle of the invention is based on a selection 11 of an optimal recognition mode, among at least three recognition modes, taking into account the recognition result or results from a or more than one merge step for issuing a recognition decision D.
More specifically, a recognition method according to the invention comprises a first melting step 12 delivering a first recognition result 121, and at least a second melting step 13 delivering a second recognition result 131.
The selection step 11 makes it possible to select a recognition mode corresponding to a melting step taken independently (for example the first step 12 or the second step 13) or to a combination of at least two of the melting steps (for example 14). This selection step 11 may be carried out after the various melting steps, in particular to take account of the result of these different steps, or prior to the melting steps.
The decision step 15 then makes it possible to deliver a recognition decision D according to the recognition result of the selected mode.
The recognition method according to the invention is implemented in the identification / authentication phase of the recognition of individuals.
The technique according to the invention makes it possible to take into account in particular the parameters of the environment in which the biometric measurements are made, as well as the desired level of security.
5.2 Functional architecture The functional architecture of a recognition system embodying the recognition method of FIG. 1 according to a particular embodiment of the invention is now presented in relation to FIG. 2A.
Such a recognition system notably comprises M client servers 211, 212, ... 2IM1 a central server 20, and N biometric servers 221, 222, ... 22N (M> = 1, N> = 2).
5.2.1 Client servers
The client servers 21 1, 212, ... take biometric samples, or biometric measurements, of a user to be identified, which are then sent to the central server 20 via a specific XML request. The client servers 21 \, 212, ... then receive an XML response from the central server 20, containing the identifier of the recognized person when the recognition decision was made.
The client servers are for example: a videotelephony server 211: it manages calls from videophones and directs the user on the online services.
It communicates with the central server to identify / authenticate the users wishing to connect to the videophone services, for example by using the results of biometric measurements of the biometric servers 22 1, 222,..., For example of the vocal and facial type;
A VoiceXML 2I2 voice server: its role is the same as the video telephony server but the identification / authentication of the user is based solely on the voice.
As indicated above, the biometric measurements from the client servers 21 1, 2 I 2,..., Are transmitted to the central server 20 which directs them to the biometric servers 221, 222, .... These biometric servers return to the central server 20 biometric measurement results, allowing the server 20 to make a recognition decision D.
This decision is then transmitted to the client server. 5.2.2 Biometric servers Biometric servers, also known as biometric platforms (referred to as PFBs), are involved in the two phases of the recognition of an individual. In the first enrollment phase, each PFB records fingerprints (voice / facial / digital ...) of a user, according to the learning characteristics of each PFB. For example, some facial recognition engines need at least three images for the generation of a facial imprint, others only need one image. In general, speech recognition servers only need three repetitions of a statement to generate a voice print.
In the second identification / authentication phase, the PFBs receive the biometric samples from the central server 20, perform recognition calculations and send the recognition results to the central server. In this phase too, each PFB has its own needs in terms of the number of repetitions of a statement (for speaker recognition) or the number of images (for facial recognition) necessary for the identification / authentication of a speaker. individual.
The biometric platforms are for example: a facial PFB 22 \ which makes it possible to identify and authenticate a person by the recognition of his face:
it uses a recognition technology as described in the patent document FR 05 03047, not yet published, filed in the name of the same applicant as the present application, and based on a statistical method of recognizing faces in the digital images on which is applied two-dimensional linear discriminant analysis (ADL2D). Technically, the facial PFB can be for example an IBM Xseries 335 (registered trademark), with a configuration Windows XP (registered trademark), following the HTTP protocol; a voice PFB 222 <0> ^ is used to identify and authenticate a person by voice: it uses speaker-based speech recognition technology based on MFCC (Mel-Frequency Cepstrum Coefficients) coefficients for coding and HMM ("Hidden Markov Models") for decoding.
This technology is dependent on the text used during the first phase of enrollment of the recognition of individuals, that is to say that the text used during the identification / authentication phase must be the same as that used during the authentication phase. enrollment phase. On the other hand, this technology is independent of the content itself of the chosen text.
Technically, the voice PFB can be for example a Compaq server (registered trademark) with a RAIDI configuration - Intel Xeon 2.5GHz (registered trademark), following the TCP protocol; a digital PFB 223 which makes it possible to identify a person by his fingerprints: he uses a technology based on the "MorphoModule" (registered trademark) solution of SAGEM
(version 3.0) which consists of a digital sensor Sagem MMI lO (registered trademark), an RS232 interface and a driver (driver) to exploit the features of the sensor. The system "MorphoModule" (registered trademark) provides a number of commands including digital identification. These commands are accessible from an RS232 communication interface via the MM110 (registered trademark) protocol.
Technically, the digital PFB can be for example an IBM XSeries 345 server (registered trademark) with a RAIDI configuration - Intel Xeon 2.5GHz (registered trademark), following the TCP protocol. The Sagem MM110 (registered trademark) digital sensor is installed on the customer's PC via an RS232 interface. 5.2.3 Central Server As illustrated in FIG. 3, it is composed in particular of a front-end computer, conventionally called "front-end", 31, of a core 32 as well as a multimodal biometric fusion system 1 object of the present invention.
This application server can be implemented in a Java J2EE (registered trademark) environment, and can be an IBM Xseries 35, with the following configuration: 1 GB RAM-2x36 GB DD - RAIDI - Intel Xeon 2.8GHz (registered trademark).
This server receives XML requests from the client servers 211, 212, ... in order to perform the identification / multimodal biometric authentication of an individual.
The "front-end" 31 is the entry point of the central server 20. It hosts the web administration interfaces which allow to configure and supervise the central server.
It may also include a database for maintaining the query history from all client servers as well as registering the profile of the users or groups of users.
The core 32 manages all exchanges with the PFB 22 [chi], 222, - - - H <>> including the transmission of speech files, image ... to all PFB.
The multimodal biometric fusion system 1 is for example in the form of a biometric fusion software module. In particular, it makes it possible to analyze the results of biometric measurements delivered by the PFBs and to establish a diagnosis on the identification / authentication of an individual.
The merge algorithm is described more specifically in sections 5.3 and 5.5, as well as in Appendix C, which forms an integral part of this specification.
5.2.4 Client server exchanges <-> central server and central server <-> PFB As illustrated in connection with Figure 2B, the exchanges between the client servers and the central server, as well as those between the central server and the PFB, follow according to this exemplary embodiment, the XML formalism, according to a previously defined scheme.
For example, biometric samples (audio / video ...) are sent by the client servers to the central server via an XML SendData Test request.
Other examples of queries used in the invention are described in Annexes A and B, which form an integral part of the present description.
The central server simultaneously accesses all the PFBs.
According to this example of use, the request 23 of the videophone client server 21 to the central server 20 follows the HTTP protocol. This request makes it possible, in particular, to send the samples or biometric measurements to the central server 20. The latter sends a request 23 \ to the facial PFB 22 j according to the HTTP protocol and a simultaneous request 232 <to> l <a> PFB voice 222 following the TCP protocol.
The facial PFB 22 \ returns a biometric measurement result to the central server 20 via a request 241 and the voice PFB 222 returns the biometric measurement result to the central server 20 via a request 242.
After having implemented the method of fusion recognition according to the invention, the central server 20 returns a recognition decision to the video call client server 21 via a request 24.
Thus, according to this embodiment of the invention, an easy-to-use XML interface has the following advantages: a multimodal and unique XML interface, whatever the PFBs used; complete multimodal system through simultaneous access to PFBs; - generic architecture that provides more flexibility in adding and / or modifying a PFB. 5.2.5 Biometric measurement results
Following a request for identification / authentication, each PFB 22 \,
222 provides a response to the central server 20, according to a standard or proprietary formalism, language or protocol. For each PFB, these responses may or may not be described in the same format.
A pretreatment may be carried out in particular to extract the relevant elements required as a result of the recognition, these elements being called biometric measurement results and used by the central server 20 for the fusion algorithms. These results of biometric measurements are, for example, distance measurements, recognition scores, posterior probabilities ... associated with identifiers of individuals.
A particular example is that of the best measures ("NBest" in English).
5.3 Merge Algorithms, Recognition Mode Selection and Recognition Decision
As illustrated by FIGS. 4 and 5, the melting system 1 comprises the following main steps: initialization 41; selecting a recognition mode 11, among at least three modes; fusions 12, 13, 14; recognition decision 15.
The initialization step 41 makes it possible to initialize all the data structures necessary for the mergers.
For example, according to the embodiment described, the initialization step 41 initializes the following variables:
X: real type matrix comprising N couples [ID, P], where ID is a vector containing the x identifiers rendered by each PFB, and P is a vector containing the x biometric measurement results rendered by each PFB; - merge: integer determining the type of fusion used; rule: integer that determines a merge rule to use;
SL: Integer determining a level of security required during the decision;
SigQual: real vector including a measure of quality for each of the N PFBs; [omega]: real type vector comprising all the N weighting coefficients; [beta]: real determining a global decision threshold; [Delta]: real determining an inter-individual decision threshold; - [epsilon]: real determining a threshold of quality of the input signal (for example level of saturation of the signal, signal / noise ratio, ...).
The system can also use several measurements of the signal quality for the same modality (for example a voice PFB can calculate the saturation index of the speech signal, the level of the signal-to-noise ratio, etc.);
FD: real type matrix comprising the result of the merger of the set of distances [IDd, fd], where fd is a vector containing the x distance measurements;
FO: real type matrix including the result of the fusion of the set of opinions [IDop, fo], where fo is a vector containing the x measures of the fusion of opinions;
D: integer containing the user's identifier, or an error code in the opposite case.
The selection step 11 consists in selecting one or the other of the fusion methods 12, 13 or a combination 14 thereof, according to predetermined parameters among the following:
a desired level of security. According to one particular embodiment of the invention, three security levels are defined: high security: the most robust setting (low false identification / acceptance rate, high rejection rate); o average security: compromise between robustness and ergonomics; o Low security: the most ergonomic setting (low rejection rate, higher false identification / acceptance rate);
- possibly a type of biometric measurements; a quality level of at least one biometric measurement: for example the saturation or contrast level of an image in the case of the biometric measurement for the facial PFB, the signal-to-noise ratio of the audio sample in the case biometric measurement for vocal PFB ...;
a quality level of at least one of the recognition results resulting from the fusion methods: the system can determine a threshold below which a recognition result will not be taken into account because considered as not significant; maximum execution time: this ergonomics parameter also intervenes in the security level and makes it possible to take into account the particular and specific use of the recognition of individuals. Some client servers may have maximum access time constraints to the services secured by the recognition of individuals and the consideration of this parameter is then necessary in the selection of the recognition mode, which allows to choose the mode of recognition the faster.
Combinations of these different parameters make it possible to choose the mode of recognition best suited to each particular context of recognition of individuals, to each environment. For example, a system for recognizing individuals at the entrance of a highly secure building using voice and facial PFBs, with a merger system using a merger of opinions and a fusion of distances, can take into account, in particular, the parameters following:
- high security level (product rule used for the merger of opinions); quality level of the two biometric measurements used (eg signal-to-noise ratio for the voice and contrast level for the facial image);
no time constraint (combination of the recognition results resulting from the two fusion rules implemented);
- high thresholds of acceptability for the result of the distance merger and for the result of the merger of opinions. Once the fusion method (s) selected, the fusion step implements the various fusion algorithms, which use the results of biometric measurements delivered by the different PFBs.
In a particular embodiment of the invention, the fusion system implements a distance fusion 12, a merger of opinions 13 and a combination of these two fusion algorithms 14.
The distance merging corresponds in particular to a combination of ordered lists of the distances of each group member with respect to the best biometric measurement result.
At the best result of biometric measurement, that is to say at most probable, we assign a distance equal to zero, the second best we assign a distance equal to one ...
Merger of opinions is a combination of the results of weighted biometric measurements. The weighting rule used is configurable by the system according to predetermined parameters and may be, for example, a weighted sum or a weighted product. The weighting coefficients used are initialized in the initialization step 41 as a function of predetermined parameters.
The calculation time for the merger of opinions is longer than for the distance merge, but the recognition result is more accurate.
In order to recognize the identity of the individual, the recognition decision can be made either from a combination of the recognition results obtained by each fusion method described above, or from the recognition result provided by one either of these methods of fusion.
The decision step 15 delivers the recognition decision D, as a function of a predetermined decision threshold, which also depends on parameters such as the desired level of security or the environment of the recognition of the individual. The recognition decision is sent to the client servers having made the recognition requests, in the form of the identifier of the recognized individual D1, or a D2 error message in the event of failure of "identification.
An exemplary implementation is described in Appendix C, which forms an integral part of the present description.
5.4 Examples of Implementation of the Invention
Examples of distance merging and opinion merging are described below, as well as an example of combinations of these two mergers.
Subsequently, we assume that [sum] Pi = l and [sum] [omega] j = 1 for i: l..N. 5.4.1 Merge distances 126.96.36.199 Decision speed: high / security level: medium
For example, consider a system configuration such that: N = 2, x = 3, merge = 0, itmax = 1, [beta]> 1, [Delta] <1.
Recital: X: [Xi, X2], with:
<EMI ID = 21.1>
<EMI ID = 21.2>
<EMI ID = 21.3>
We obtain :
FD: [Di] + [D2]
<EMI ID = 21.4>
Moreover, since [beta] <1 and [Delta]> 1, then D = IDi, this [phi] means that the individual IDi <es> t <a> is the best.
188.8.131.52 Decision speed: average / security level: high
For example, consider a system configuration such that: N = 2, x = 3, merge = 0, SL = 2, itmax = 2, [beta] <= 1, [Delta] <1.
Recital X: [X1, X2], with:
<EMI ID = 22.1>
<EMI ID = 22.2>
<EMI ID = 22.4>
We obtain: FD: [Di] + [D2]
<EMI ID = 22.5>
Moreover, since [beta] <= 1 but [Delta] <1, then D = no decision, which means that:
- since SL> 1, if it <itmax then we carry out a new iteration with fusion equal to fusion _opinion, - otherwise: D = Failed (rejection of the individual) then we apply a behavior defined by default for each application: the algorithm can for example make the L best results of FD, generate an error message, etc. 5.4.2 Merger of opinions
184.108.40.206 Decision speed: average / security level: high Consider, for example, a system configuration such that: N = 2, x = 3, merge = 1, rule = 1, SL = 2: ([beta] = 0.65 , [Delta] = 0.1, [omega] l = 0.7, [omega] 2 = 0.4).
Recital X: [Xi, X2], with:
<EMI ID = 22.3>
<EMI ID = 22.6>
<EMI ID = 22.7>
<EMI ID = 23.1>
<EMI ID = 23.2>
We obtain :
Pl P2 FO
ID1 Pn = O. 6 P21 = O.7 FOi = [omega] i * Pn + [omega] 2 * P21 = 0 .7
ID2 Pl2 = O. 3 P22 = 0 .2 FO2 = [omega] i * Pi2 + [omega] 2 * P22 = 0. 29
ID3 Pl3 = O. 1 P23 = O.1 FO3 = [omega] i * Pi3 + [omega] 2 * P23 = 0. 11
Given that FOI> [beta] and [FOI - FO2]> [Delta] -> D = ID1: the individual ID1 is a priori the best.
5.4.3 Combination of fusion mechanisms
220.127.116.11 Decision speed: low / security level: very high If we repeat the configuration of paragraphs 18.104.22.168 and 22.214.171.124, as in both cases the recognized identity is ID1 the algorithm decides (recognizes) D = ID1.
126.96.36.199 Decision speed: low / security level:
very high If we repeat the configuration of paragraphs 5.4.1..2 and 188.8.131.52, as in both cases the recognized identity is not the same, the system generates a warning signal "WARNING" and a solution relief can be applied. 5.4.4 Example of weighting rules
If we consider the results of two biometric measurements P1 and P2, such as P2 "P1, the fusion result of opinions is quite different depending on the choice of the weighting rule.
For example, consider the following parameters: P1 = O-S5 P2 = O-OO15 W1 = O-S5 W2 = O-S
Merging opinions with a product weighting rule gives the following merge result:
FO = (P1 <0> '<5> V (^ 2 <0>' <5> V 0.7 x 0.0031 - 0.0221
It can be seen that the result of the second biometric measurement (P2) penalizes the first biometric measurement (P1).
Merging opinions with a sum weighting rule gives the following merge result:
FO = P1 x 0.5 + P2 x 0.5 - 0.25 + 0.0005 - 0.2505
In this case, even if P2 "Pi, the second biometric measurement is less severe with the first biometric measurement than when using the product rule.
5.5 Structure of a part of the server
The simplified structure of a part of the server implementing a technique for recognizing an individual according to the particular embodiment described above is now presented in relation to FIG.
Such a server comprises, among others, a memory 61, a processing unit 62, equipped for example with a microprocessor [mu] P, and driven by the computer program 63, implementing the recognition method according to the invention.
At initialization, the code instructions of the computer program 63 are for example loaded into a RAM before being executed by the processor of the processing unit 62. The processing unit 62 receives as input at least a biometric measurement. The microprocessor of the processing unit 62 implements the steps of the recognition method described above, according to the instructions of the computer program 63.
The processing unit 62 outputs a decision D corresponding evening to an individual identifier recognized by either an identification error code.
APPENDIX A XML request example from a client server to the central server
<ServiceId> serviceIdValue </ serviceId>
<identMode> IDENTIFICATION »AUTHENTICATION </ identMode>
<GroupId> groupIdValue </ groupId>
[<secLevel> HIGH> MEDIUM> LOW </ secLevel>] [<userId> userIdValue </ userId>] [<voice>
<filename> voiceFile 1 </ filename> <filename> voiceFile2 </ filename>
<Filename> voiceFileN </ filename>
<filename> faceFile 1 </ filename> <filename> faceFile2 </ filename>
<Filename> faceFileN </ filename>
<filename> otherFile 1 </ filename> <filename> otherFile2 </ filename>
<filename> otherFileN </ filename> </ other>] </ sendDataTest> </ request> </ simbad> APPENDIX B XML response example from the central server to the client server
<response status = OK>
<Decision> COMPLETE "INCOMPLETE" ERROR
textError </ decision>
[<groupId> groupIdValue </ groupId>] [<userid value = "userIdValueN" score_global = "scoreValue"> </ userId>] [<details> [<voice>
[groupldl userldl scorel], [groupldl userld2 score2], [groupldl userld3 score3] "INCOMPLETE] ERROR Description </ voice>] [<face>
[groupldl userldl scorel], [groupldl userld2 score2], [groupldl userld3 score3] »INCOMPLETE] ERROR Description </ face>] [<others>
[groupldl userldl scorel], [groupldl userld2 score2], [groupldl userld3 score3] »INCOMPLETE! ERROR Description </ others>] </ details>] </ response> </ simbad> APPENDIX C
Example of implementation of the fusion algorithms according to a particular embodiment of the invention
// initialization of the biometric data merging procedure
// loading input parameters
// initialize rule values,
fusion and SL
// merge can follow a pre-established // variable initialization new_iteration // function of itmax if it> itmax new iteration = FALSE otherwise new_e ration = TRUE if merge equals merge _opinions // calculate merge of opinions goto Fusion Opinion otherwise if merger equal to fusion_distances // calculate merge distance goto FusionDistance otherwise if merge equal to combination_fusions // calculate combination of merge mechanisms goto FusionDistance goto FusionOpinion else
// calculate the combination of goto merger mechanisms FusionDistance goto FusionOpinion
// order Xj = (IDj, Pj) according to the values of Pj // calculation of the distance matrix // initialize D
// calculate matrix D with N couples [id, d] j
<EMI ID = 27.1>
distance measure) for each pair [ID, P] j of table X (i = 0..N-1) for each individual (j = O..x-1)
<EMI ID = 27.2>
// different probabilities by following a // descending order Id1Q] <r ID1Q] j ^ j + 1 else: // equal consecutive probabilities
<EMI ID = 28.1>
Id1Q] ^ ID1Q]
<EMI ID = 28.2>
j ^ j + 2
// compute the distance merge matrix FD = // [id ^ fd] // initialize vector FD for each of the x values of ido [j] for i = 0 ..
N-I // fdQ] equal to the sum of N values dj // associated with ido [j]
IDdDI <r Id0G] // order vector FD following a descending order in // function of the values of fd
// calculate the opinion merge vector
// initialize vector of weighting coefficients // [omega] [i] for each pair [ID5P] 1 of array X (i = 0..N - // l) according to the rule (eg sum / product // weighted), the safety level SL, and the level of // quality of the input signal if N> 1
<EMI ID = 28.3>
// pre-established value [omega]  <- default coef
// initialize vector FO = [IDOp, fo] for each of x IDQ values for i = 0 ..
N-I if rule equal to sum _weighted
II fo [j] sum of N values of Pj // associated with IDQ [J]
<EMI ID = 28.4>
otherwise: if rule equal to product _weighted foQ] <r foQ] * (PiQ]) <[Lambda]> [omega] [i] otherwise
// apply another rule
<EMI ID = 29.1>
j ^ j + l
// order vector FO according to a descending order in function of the values of fo
The values of the [beta] and [Delta] thresholds will depend on SL, merge and rule.
// estimate of the identity of the individual, decision // initialize the thresholds ([beta], [Delta]) if fusion equals FUSION_DISTANCES // merge distance decision
IDmax << ¯> IDj 0 = 1 -x) <tel> q <ue FD> minD <">] bmin
if FDOW1) <= [beta] if (FDGmin-1) - FDOmin))> [Delta]
Y <r IDmax Yd = Y otherwise: if SL <1 if new iteration equal to TRUE goto Initialization otherwise
// trigger WARNING otherwise: // apply default solution otherwise:
if new iteration equal to TRUE goto Initialization otherwise // trigger WARNING if merger equals FUSION OPINIONS: // merge opinion decision
IDmax <- IDj G = I-M) such that FOmax »j]  max
<EMI ID = 29.2>
if (FOGmax) "FOGmax" I))> [Delta] Y <r IDmax Yo = Y otherwise: if SL <1 if new iteration equal to TRUE goto Initialization otherwise
// trigger WARNING otherwise:
// apply default solution otherwise: if new iteration equal to TRUE goto Initialization otherwise
// trigger WARNING otherwise:
// combination merger mechanisms goto decision_fusion_distances goto decision_fusion_opinions // compare identity Y recognized for both mechanisms if Yo equals Yd
// Y identity recognized Y = Yo = Yd otherwise // trigger WARNING