EP1960973B1 - Method of securing a physical access and access device implementing the method - Google Patents

Method of securing a physical access and access device implementing the method Download PDF

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Publication number
EP1960973B1
EP1960973B1 EP06829334A EP06829334A EP1960973B1 EP 1960973 B1 EP1960973 B1 EP 1960973B1 EP 06829334 A EP06829334 A EP 06829334A EP 06829334 A EP06829334 A EP 06829334A EP 1960973 B1 EP1960973 B1 EP 1960973B1
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European Patent Office
Prior art keywords
parameters
fraud
type
access
values
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EP06829334A
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German (de)
French (fr)
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EP1960973A1 (en
Inventor
Emmanuel Bernard
Jean-Christophe Fondeur
Laurent Lambert
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Idemia Identity and Security France SAS
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Morpho SA
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    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C9/00—Individual registration on entry or exit
    • G07C9/10—Movable barriers with registering means
    • G07C9/15—Movable barriers with registering means with arrangements to prevent the passage of more than one individual at a time
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C9/00—Individual registration on entry or exit
    • G07C9/30—Individual registration on entry or exit not involving the use of a pass
    • G07C9/32—Individual registration on entry or exit not involving the use of a pass in combination with an identity check
    • G07C9/33—Individual registration on entry or exit not involving the use of a pass in combination with an identity check by means of a password

Definitions

  • the invention lies in the field of physical access control at the exits of a sensitive area and more particularly the control of the uniqueness of a person crossing a controlled passage.
  • This domain groups together two types of problematic, the first of which consists in authenticating a person presenting himself, the second consisting in ensuring that only the authenticated person crosses the controlled passage in order to guard against a fraud in which an unauthorized person takes advantage of the passage of a person authorized to sneak ("tailgating" in English).
  • FR 2 871 602 A use a ground pressure pad to determine if one or more people are on the mat and to open a door depending on the outcome of that test.
  • the invention aims to improve the detection rate of fraud attempts during the passage of a person in a controlled space. It is based on the use of different sets of parameters from at least two different sensor systems, some of these sets of parameters being based on correlations of measurements from these different sensor systems. An apprenticeship is made to characterize different types of fraud and then allow the identification of a fraud attempt by correlation between the measurements obtained and the characterizations of each type of fraud for each set of parameters.
  • the probability of fraud associated with each type of fraud for each set of parameters is estimated by calculating a distance between the set of values determined during this access and the class corresponding to the type of fraud for this set of parameters.
  • this distance is an algebraic distance between the determined set of values and the centroid of the class.
  • the probability of fraud associated with each type of fraud for each set of parameters is estimated by a neuromimetic network and where the class learning determination step comprises a training step of this neuromimetic network.
  • the sensor systems comprise a camera system (1.5, 1.6) providing profile images (1.8, 1.9, Fig. 3 ).
  • the sensor systems comprise a ground pressure carpet system (1.4) providing pressure images (1.7, Fig. 4 ).
  • the measuring means used can be of all kinds: pressure sensor, temperature, optical means (camera, laser beams ). Similarly, the measurement analysis can be more or less consolidated (combined or independent use of the data), interpreted (taking into account dynamic or static factors), etc.
  • the system described here is based on a uniqueness detection system using a ground pressure pad.
  • the interest of a system of this type is to observe the contact with the soil and its evolution over time in order to be able to deduce the number of people present according to the traces present on the ground and their evolution. Nevertheless, there are very simple ways to defraud such a system by reducing ground contacts. For example, two people can pass simultaneously if they are close enough to each other.
  • the object of the invention is to consolidate the existing uniqueness detection using a combination of ground pressure sensors and cameras and / or profile detection, and to deal with fraud attempts with a data fusion algorithm. and behavioral analysis of detected objects.
  • the algorithm makes it possible to classify the passage according to the type of possible attacks by comparing the measurements made and the various classes associated with the types of fraud envisaged, the decision of fraud or not is then taken according to the class.
  • the invention is carried out within an airlock controlling an access.
  • This airlock is shown schematically Fig. 1 .
  • a person 1.1 crosses the airlock from left to right.
  • the airlock is equipped with a number of sensor systems.
  • We call sensor system a system for the acquisition of information and based on a plurality of sensors of the same type.
  • the airlock is equipped at ground level with a first sensor system consisting of a pressure sensitive mat 1.4.
  • This carpet provides a two-dimensional pressure image 1.7 providing at each of its points the value of the pressure exerted.
  • An example of these pressure images is shown Fig. 4 .
  • the airlock is also provided with a second sensor system consisting of video cameras 1.5 and 1.6. These cameras are two in the embodiment, but their number may be higher or lower depending on the amount of information that is desired. One can, in particular, add a camera on the top. These cameras provide profile images 1.2, 1.3 to determine profiles 1.8, 1.9 associated with people or objects in the airlock.
  • the floor and the walls of the airlock can be of saturated colors in order to limit the problems induced by the shadows carried by the persons or objects present in the airlock.
  • An example of a profile image is shown Fig. 3 .
  • the airlock is, moreover, generally provided with authentication means not shown as a badge reader or biometric identification means such as an iris reader of the eye or fingerprints.
  • the airlock is typically connected to data acquisition means produced by the sensor systems, means for analyzing these data, decision-making and control.
  • These means may consist of a computer 1.9 which is provided with a hard disk for storing received images, both pressure and profiles, as well as programs necessary to process these images and extract the parameters that are used to determine whether the passage is validated or not.
  • this computer can, for example, allow the opening of a door located at the end of the lock. Otherwise, the door remains closed and an alarm can be sent to a monitoring station or other.
  • a person wishing to defraud and therefore enter without authorization usually tries to take advantage of the passage of an authorized person to sneak through the door via the airlock. This attempt may be made without the knowledge of the authorized person assume, for example, that the next person is also authorized. This attempt can also be made with the complicity of the authorized person or by coercion. It is therefore for the fraudster to try to deceive the sensor systems trying to hide his passage. To do this, he can try to stick to the first person, for example back-to-back, to deceive the cameras and stick his feet to those of the first person so that the system only distinguishes two "large" footprints , see for example the pressure picture Fig. 6 . We will call this type of fraud "stuck fraud”.
  • the fraudster may also attempt to squat, or by staying exactly on the side of the authorized person. Some special cases may also cause problems in recognizing a child next to an adult or even a baby in the arms of his mother. These fraud attempts are only examples of possible types of fraud.
  • the challenge of the system is therefore to succeed in discriminating the valid passages of a single person and this regardless of the size, the body, the dress or the luggage of this person of an attempt of fraud like those which we come from to describe.
  • parameters can be data directly derived from the sensors or parameters calculated from the information provided.
  • the camera system it is possible to obtain from the images taken, so-called profile images. These images are obtained by discriminating the subject from the background. The necessary digital image processing techniques are known. Once these images of profiles obtained, it is possible to extract parameters as illustrated by the Fig. 3 . The location of the center of gravity 3.3 of the object 3.2, its height 3.6, its width 3.5 is easily obtained. By analyzing the images over time, it is also possible to extract the average speed 3.4 from the center of gravity. It is also possible to apply an algorithm to count the heads, in fact an algorithm that will count the excrescences of the profile 5.1 in its upper part. By crossing profiles from several cameras, it is still possible to calculate the volume of the object, as well as the distribution of this volume according to the height of the object. One can, for example, choose to divide the height in three equal parts and determine the percentage of the volume located in the lower part, the middle part and the upper part of the object. These parameters are only examples of the possible parameters from the camera system.
  • parameters are extracted from the sensor system constituted by the pressure belt.
  • Pressure images such as those illustrated Fig. 4 , here also make it possible to obtain for each object 4.2, its height 4.6, its width 4.5 and the overall center of gravity of the detected objects 4.3.
  • a study of the evolution over time of the objects makes it possible to calculate the average displacement speed 4.4 of this center of gravity as well as the average over time of the previous values. It is also possible to calculate an overall height and width.
  • An integration of the pressure values allows an estimation of the total weight of the objects present in the airlock.
  • the fact of using at least two sensor systems makes it possible to calculate additional parameters resulting from the correlation of information provided by each of the sensor systems. It is for example possible to establish a volume / weight ratio of the objects present in the airlock, or the difference in speed of movement between the objects detected by the cameras and the objects detected by the pressure belt. It is also possible to compare the positions and the number of ground contacts with the objects detected by the cameras.
  • Fig. 6 step 6.1 A choice is made among all these possible parameters.
  • the selected parameters from a sensor system are matched to a set of parameters.
  • the parameters resulting from the correlation between two sensor systems will also provide a set of parameters.
  • the system is therefore able to calculate a set of sets of values for each set of parameters corresponding to this access.
  • Each set of parameters can be viewed as a multidimensional space where each dimension corresponds to a parameter.
  • the calculated values for each parameter define a vector in this space representing the set of values.
  • Fig. 2 In this figure, is represented a space of dimension three corresponding to a set of three parameters.
  • Each of the dimensions 2.1, 2.2, 2.3 thus corresponds to a parameter of the game.
  • the vector 2.3 corresponds to the values measured or calculated during a given passage.
  • the successive measurements of different passages give a collection of vectors defining a class of values corresponding to these passages.
  • Such a class 2.5 is represented Fig. 2 .
  • a class corresponding to the measurements made during a series of passages is thus defined. If such series of measurements are taken for valid passages and then for passages corresponding to fraud attempts, classes corresponding to a valid passage and classes corresponding to the types of fraud envisaged are established for each set of parameters. We obtain as illustrated Fig. 6 , step 6.2, and for each set of parameters, a class corresponding to the various fraud attempts.
  • a distance measurement between the values of measured and / or calculated parameters of a set of parameters and the different classes corresponding to the different types of passages can be a simple algebraic distance between the measured vector and the centroid of the vectors of the class or any other measure of distance in space.
  • This measure of distance can be a simple algebraic distance between the measured vector and the centroid of the vectors of the class or any other measure of distance in space.
  • a probability that the passage belongs to the class considered as illustrated Fig. 6 step 6.4.
  • Each set of parameters is thus classified and a probability is associated with this classification.
  • the classification of the passage is carried out by consolidation of the classifications obtained for each set of parameters, as illustrated Fig. 6 step 6.5.
  • the classification steps of a set of parameters can be performed by a formal neural network, otherwise known as a neuromimetic network.
  • a formal neural network otherwise known as a neuromimetic network.
  • These networks operate on the model of an interconnection of formal neurons, each of its formal neurons performing a weighted sum of its inputs and applying to this sum a nonlinear output function which may be a simple threshold or a more sophisticated function such as sigmoid function.
  • the knowledge or information stored in the network corresponds to the synaptic weights of each neuron, these weights being calculated by learning. This learning is done using a "training" algorithm which consists in modifying the synaptic weights according to a data set presented at the input of the network. The purpose of this training is to allow the neural network to "learn" from the examples.
  • the network is able to provide output responses very close to the original values of the training data set.
  • all the interest of neural networks lies in their ability to generalize from the test game.
  • Such a network of neurons driven on the passages constituting the classes during a learning phase is thus able to reliably perform a classification of the passages and to give for each passage a probability associated with each set of parameters and each passage or access.
  • the invention while describing the use of a pressure and camera belt, may likewise include different sensor systems such as infrared or laser barriers, infrared cameras, diodes or any other means of obtaining information about objects or bodies present in a control space.
  • the described invention aims to discriminate the uniqueness of a person's presence, but it could just as easily apply to other criteria, such as the uniqueness of a vehicle or others.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Burglar Alarm Systems (AREA)
  • Alarm Systems (AREA)
  • Image Analysis (AREA)
  • Debugging And Monitoring (AREA)
  • Collating Specific Patterns (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
  • Closed-Circuit Television Systems (AREA)

Abstract

A method of improving the rate of detection of attempts at fraud when a person passes through a controlled space based on the use of different sets of parameters issuing from at least two different sensor systems, some sets of parameters being based on correlations of measurements issuing from various sensor systems. Learning is carried out so as to characterize various types of fraud to permit identification of attempts at fraud by correlation between measurements obtained and characterizations of each type of fraud for each set of parameters.

Description

Domaine techniqueTechnical area

L'invention se situe dans le domaine du contrôle d'accès physique aux issues d'une zone sensible et plus particulièrement du contrôle de l'unicité d'une personne franchissant un passage contrôlé. Ce domaine regroupe deux types de problématiques, une première consistant à authentifier une personne se présentant, le second consistant à s'assurer que seule la personne authentifiée franchit le passage contrôlé de façon à se prémunir d'une fraude où une personne non autorisée profite du passage d'une personne autorisée pour se faufiler (« tailgating » en anglais).The invention lies in the field of physical access control at the exits of a sensitive area and more particularly the control of the uniqueness of a person crossing a controlled passage. This domain groups together two types of problematic, the first of which consists in authenticating a person presenting himself, the second consisting in ensuring that only the authenticated person crosses the controlled passage in order to guard against a fraud in which an unauthorized person takes advantage of the passage of a person authorized to sneak ("tailgating" in English).

Art antérieurPrior art

Il est connu du document EP 0 706 062 une méthode de détection d'unicité dans un sas. Cette méthode couple un lecteur de tickets permettant la validation d'un titre de transport et une détection par ultra son. Un seul type de capteur est utilisé.It is known from the document EP 0 706 062 a method of detecting uniqueness in an airlock. This method couples a ticket reader allowing the validation of a ticket and an ultra sound detection. Only one type of sensor is used.

Il est connu du document US 2002/097145 A1 une méthode de sécurisation d'un accès basée sur l'authentification des personnes par un seul système de capteurs. On ne cherche pas à assurer l'unicité du passage.It is known from the document US 2002/097145 A1 a method of securing access based on the authentication of people by a single sensor system. We are not trying to ensure the uniqueness of the passage.

Il est connu du document WO 03/088157 A une méthode de sécurisation d'un accès par analyse d'image. Une détection des objets est faite, ces objets sont classés et des caractéristiques en sont extraites pour déterminer des tentatives de fraude.It is known from the document WO 03/088157 A a method of securing access by image analysis. An object detection is done, these objects are classified and features are extracted to determine fraud attempts.

Il est connu du document FR 2 713 805 A un système de contrôle d'accès disposant de trois différentes zones. Dans une première zone dite de péage, les utilisateurs s'acquittent du paiement. Dans une seconde zone, les personnes sont comptées. Dans une troisième zone, dite de franchissement, une barrière peut se refermer dans le cas où le nombre de personnes comptées est supérieur au nombre de paiement. Le but est ici de compter les personnes et non d'identifier des types de fraude.It is known from the document FR 2,713,805 A an access control system with three different zones. In a first toll zone, the users pay the payment. In a second zone, people are counted. In a third zone, called a crossing zone, a barrier can close in the case where the number of people counted is greater than the number of payments. The goal here is to count people and not to identify types of fraud.

Il est connu de FR 2 871 602 A d'utiliser un tapis de pression au sol permettant de déterminer si une personne ou plusieurs se trouvent sur le tapis et de commander l'ouverture d'une porte en fonction du résultat de ce test.It is known to FR 2 871 602 A use a ground pressure pad to determine if one or more people are on the mat and to open a door depending on the outcome of that test.

Il est connu de par le document EP 1 100 050 A1 des systèmes de comptage de personnes empruntant une entrée par traitement d'images vidéo. Dans ce document, un seul type de capteur est utilisé. Il est également connu de par le document US 2002/0067259 A1 d'utiliser plusieurs types de capteurs pour déterminer la présence d'une personne et son unicité. Dans ce document, il est décrit de corréler les données de plusieurs capteurs, une configuration de coupure de faisceaux et un détecteur de chaleur, pour détecter un objet non humain de façon à discriminer une personne avec bagage d'une intrusion. Le document US 2004/0188185 , quant à lui, décrit de corréler les informations d'une image de chaleur et d'une image optique pour compter le nombre de personnes présentes dans un espace. Dans le document EP 1 308 905 A1 est décrit l'utilisation d'un tapis sensible à la pression pour détecter la présence de personnes, leur sens de déplacement, et effectuer un comptage à partir des données du tapis et de leur évolution dans le temps.It is known from the document EP 1 100 050 A1 systems for counting people using an input by video image processing. In this document, only one type of sensor is used. It is also known from the document US 2002/0067259 A1 to use several types of sensors to determine the presence of a person and his uniqueness. In this document, it is described to correlate the data of several sensors, a beam cut-off configuration and a heat detector, to detect a non-human object so as to discriminate a person with luggage from an intrusion. The document US 2004/0188185 as for him, describes to correlate the information of a heat image and an optical image to count the number of people present in a space. In the document EP 1 308 905 A1 is described the use of a pressure sensitive mat to detect the presence of people, their direction of movement, and make a count from the carpet data and their evolution over time.

Ces méthodes ne sont toutefois pas suffisantes pour détecter avec fiabilité les tentatives de fraude d'une personne déterminée.However, these methods are not sufficient to reliably detect attempts at fraud by a particular person.

Exposé de l'inventionPresentation of the invention

L'invention vise à améliorer le taux de détection des tentatives de fraude lors du passage d'une personne dans un espace contrôlé. Elle est basée sur l'utilisation de différents jeux de paramètres issus d'au moins deux systèmes de capteurs différents, certains de ces jeux de paramètres étant basés sur des corrélations de mesures issues de ces différents systèmes de capteurs. Un apprentissage est fait de façon à caractériser différents types de fraudes pour ensuite permettre l'identification d'une tentative de fraude par corrélation entre les mesures obtenues et les caractérisations de chaque type de fraude pour chaque jeu de paramètres.The invention aims to improve the detection rate of fraud attempts during the passage of a person in a controlled space. It is based on the use of different sets of parameters from at least two different sensor systems, some of these sets of parameters being based on correlations of measurements from these different sensor systems. An apprenticeship is made to characterize different types of fraud and then allow the identification of a fraud attempt by correlation between the measurements obtained and the characterizations of each type of fraud for each set of parameters.

L'invention concerne un procédé de sécurisation d'un accès physique disposant d'une pluralité de systèmes de capteurs (1.4, 1.5, 1.6), ledit procédé visant à discriminer un accès valide d'une tentative d'accès en fraude, comprenant les étapes suivantes :

  • dans une phase préliminaire :
    • détermination d'au moins un jeu de paramètres issus des systèmes de capteurs dont au moins un jeu de paramètres issus d'au moins deux systèmes de capteurs différents (6.1) ;
    • détermination par apprentissage, pour chaque jeu de paramètres et pour chaque type de fraude envisagé, d'une classe de valeurs des paramètres du jeu correspondant à ce type de fraude pour ce jeu de paramètres (6.2) ;
  • lors d'un accès :
    • détermination de jeux de valeurs formés des valeurs prises par chaque paramètre de chaque jeu de paramètres pour cet accès (6.3) ;
    • détermination d'une probabilité de fraude associée à chaque type de fraude pour chaque jeu de paramètres, en fonction du jeu de valeurs déterminé lors de cet accès et de la classe correspondant au type de fraude pour ce jeu de paramètres (6.4) ;
    • détermination d'une probabilité de fraude globale associée à l'accès en fonction des probabilités de fraude obtenues pour chaque jeu de paramètres et pour chaque type de fraude (6.5).
The invention relates to a method for securing a physical access having a plurality of sensor systems (1.4, 1.5, 1.6), said method for discriminating a valid access from a fraudulent access attempt, comprising the following steps :
  • in a preliminary phase:
    • determining at least one set of parameters from the sensor systems including at least one set of parameters from at least two different sensor systems (6.1);
    • determining by learning, for each set of parameters and for each type of fraud envisaged, a class of values of the game parameters corresponding to this type of fraud for this set of parameters (6.2);
  • during an access:
    • determining sets of values formed from the values taken by each parameter of each set of parameters for that access (6.3);
    • determining a probability of fraud associated with each type of fraud for each set of parameters, according to the set of values determined during this access and the class corresponding to the type of fraud for this set of parameters (6.4);
    • determining a probability of overall fraud associated with access based on the fraud probabilities obtained for each set of parameters and for each type of fraud (6.5).

Selon un mode particulier de l'invention la probabilité de fraude associée à chaque type de fraude pour chaque jeu de paramètres est estimée par calcul d'une distance entre le jeu de valeurs déterminé lors de cet accès et la classe correspondant au type de fraude pour ce jeu de paramètres.According to one particular embodiment of the invention, the probability of fraud associated with each type of fraud for each set of parameters is estimated by calculating a distance between the set of values determined during this access and the class corresponding to the type of fraud for this set of parameters.

Selon un mode particulier de l'invention, cette distance est une distance algébrique entre le jeu de valeurs déterminé et le barycentre de la classe.According to one particular embodiment of the invention, this distance is an algebraic distance between the determined set of values and the centroid of the class.

Selon un mode particulier de l'invention la probabilité de fraude associée à chaque type de fraude pour chaque jeu de paramètres est estimée par un réseau neuromimétique et où l'étape de détermination par apprentissage des classes comprend une étape d'entraînement de ce réseau neuromimétique.According to one particular embodiment of the invention, the probability of fraud associated with each type of fraud for each set of parameters is estimated by a neuromimetic network and where the class learning determination step comprises a training step of this neuromimetic network. .

Selon un mode particulier de l'invention les systèmes de capteurs comprennent un système de caméras (1.5, 1.6) fournissant des images de profil (1.8, 1.9, Fig. 3).According to one particular embodiment of the invention, the sensor systems comprise a camera system (1.5, 1.6) providing profile images (1.8, 1.9, Fig. 3 ).

Selon un mode particulier de l'invention les systèmes de capteurs comprennent un système de tapis de pression au sol (1.4) fournissant des images de pression (1.7, Fig. 4).According to a particular embodiment of the invention, the sensor systems comprise a ground pressure carpet system (1.4) providing pressure images (1.7, Fig. 4 ).

L'invention concerne également un dispositif de sécurisation d'un accès physique comprenant :

  • un espace de contrôle ;
  • une pluralité de systèmes de capteurs dans cet espace de contrôle (1.4, 1.5, 1.6) ;
  • des moyens d'analyse des informations issues des systèmes de capteurs (1.9) ;
et sachant qu'est déterminé au moins un jeu de paramètres issus des systèmes de capteurs dont au moins un jeu de paramètres issus d'au moins deux systèmes de capteurs différents, étant déterminée par apprentissage, pour chaque jeu de paramètres et pour chaque type de fraude envisagé, une classe d'espace de valeurs des paramètres du jeu correspondant à ce type de fraude pour ce jeu de paramètres, les moyens d'analyse comprennent :
  • des moyens de détermination de jeux de valeurs formés des valeurs prises par chaque paramètre de chaque jeu de paramètres pour cet accès ;
  • des moyens de détermination d'une probabilité de fraude associée à chaque type de fraude et pour chaque jeu de paramètres, en fonction du jeu de valeurs déterminé lors de cet accès et de la classe correspondant au type de fraude pour ce jeu de paramètres ;
  • des moyens de détermination d'une probabilité de fraude globale associée à l'accès en fonction des probabilités de fraude obtenues pour chaque jeu de paramètres et pour chaque type de fraude.
The invention also relates to a device for securing a physical access comprising:
  • a control space;
  • a plurality of sensor systems in this control space (1.4, 1.5, 1.6);
  • means for analyzing information from the sensor systems (1.9);
and knowing that at least one set of parameters from the sensor systems is determined, at least one set of parameters from at least two different sensor systems, being determined by learning, for each set of parameters and for each type of sensor. fraud envisaged, a class of game settings value space corresponding to this type of fraud for this set of parameters, the means of analysis include:
  • value set determining means formed from the values taken by each parameter of each set of parameters for that access;
  • means for determining a fraud probability associated with each type of fraud and for each set of parameters, as a function of the set of values determined during this access and of the class corresponding to the type of fraud for this set of parameters;
  • means for determining a global fraud probability associated with the access as a function of the fraud probabilities obtained for each set of parameters and for each type of fraud.

Brève description des dessinsBrief description of the drawings

Les caractéristiques de l'invention mentionnées ci-dessus, ainsi que d'autres, apparaîtront plus clairement à la lecture de la description suivante d'un exemple de réalisation, ladite description étant faite en relation avec les dessins joints, parmi lesquels :

  • La Fig. 1 représente un schéma d'ensemble d'un mode de réalisation de l'invention.
  • La Fig. 2 représente graphiquement une classe de caractérisation d'un type de fraude dans l'espace d'un jeu de paramètres selon un mode de réalisation de l'invention.
  • La Fig. 3 représente un exemple d'image de profil obtenue par une caméra.
  • La Fig. 4 représente un exemple d'image de pression obtenue par un tapis de pression.
  • La Fig. 5 représente un exemple d'image de pression correspondant à un passage suivi, dos à dos en « collant les pieds ».
  • La Fig. 6 représente un organigramme de la méthode.
The characteristics of the invention mentioned above, as well as others, will appear more clearly on reading the following description of an exemplary embodiment, said description being given in relation to the attached drawings, among which:
  • The Fig. 1 represents an overall diagram of an embodiment of the invention.
  • The Fig. 2 graphically represents a class of characterization of a type of fraud in the space of a set of parameters according to an embodiment of the invention.
  • The Fig. 3 represents an example of a profile image obtained by a camera.
  • The Fig. 4 represents an example of pressure image obtained by a pressure pad.
  • The Fig. 5 represents an example of a pressure image corresponding to a passage followed, back to back by "sticking the feet".
  • The Fig. 6 represents a flowchart of the method.

Exposé détaillé de l'inventionDetailed exposition of the invention

Dans le cadre du contrôle et de la sécurisation d'accès physiques, il est souvent crucial de vérifier qu'une personne est bien la seule à avoir franchi une porte, un couloir, un sas de sécurité, etc. On peut alors parler de détection d'unicité. Le « tourniquet » du métro ou le sas sécurisé d'un aéroport sont des exemples de mise en oeuvre de la détection d'unicité. Les moyens de mesure mis en oeuvre peuvent être de tous horizons : capteur de pression, de température, moyens optiques (caméra, faisceaux laser...). De même l'analyse des mesures peut être plus ou moins consolidée (utilisation combinée ou indépendante des données), interprétée (prise en compte de facteurs dynamiques ou statiques), etc.In the context of controlling and securing physical access, it is often crucial to verify that a person is the only one to have crossed a door, a corridor, a security lock, etc. We can then speak of detection of uniqueness. The "turnstile" of the metro or the secure airlock of an airport are examples of of the detection of uniqueness. The measuring means used can be of all kinds: pressure sensor, temperature, optical means (camera, laser beams ...). Similarly, the measurement analysis can be more or less consolidated (combined or independent use of the data), interpreted (taking into account dynamic or static factors), etc.

Le système décrit ici, est basé sur un système de détection d'unicité utilisant un tapis de pression au sol. L'intérêt d'un système de ce type est d'observer les contacts aux sols et leur évolution au cours du temps afin de pouvoir déduire le nombre de personnes présentes selon les traces présentes au sol et leur évolution. Néanmoins, il existe des moyens très simples de frauder un tel système en réduisant les contacts au sol. Par exemple, deux personnes peuvent passer simultanément si elles sont suffisamment proches l'une de l'autre.The system described here is based on a uniqueness detection system using a ground pressure pad. The interest of a system of this type is to observe the contact with the soil and its evolution over time in order to be able to deduce the number of people present according to the traces present on the ground and their evolution. Nevertheless, there are very simple ways to defraud such a system by reducing ground contacts. For example, two people can pass simultaneously if they are close enough to each other.

L'objet de l'invention est de consolider la détection d'unicité existante en utilisant une association de capteurs de pression au sol et de caméras et/ou détection de profil, et de traiter les tentatives de fraude avec un algorithme de fusion de données et d'analyse comportementale des objets détectés. Ainsi l'algorithme permet de classifier le passage selon le type d'attaques possibles en comparant les mesures faites et les différentes classes associées à des types de fraude envisagés, la décision de fraude ou non est alors prise selon la classe.The object of the invention is to consolidate the existing uniqueness detection using a combination of ground pressure sensors and cameras and / or profile detection, and to deal with fraud attempts with a data fusion algorithm. and behavioral analysis of detected objects. Thus the algorithm makes it possible to classify the passage according to the type of possible attacks by comparing the measurements made and the various classes associated with the types of fraud envisaged, the decision of fraud or not is then taken according to the class.

Dans l'exemple de réalisation décrit, l'invention est réalisée au sein d'un sas contrôlant un accès. Ce sas est représenté schématiquement Fig. 1. Une personne 1.1 franchit le sas de gauche à droite. Le sas est équipé d'un certain nombre de systèmes de capteurs. Nous appelons système de capteurs, un système permettant l'acquisition d'informations et basé sur une pluralité de capteurs du même type. Le sas est équipé au niveau du sol d'un premier système de capteurs constitué d'un tapis sensible à la pression 1.4. Ce tapis fournit une image de pression 1.7 en deux dimensions fournissant en chacun de ses points la valeur de la pression exercée. Un exemple de ces images de pression est représenté Fig. 4. Ces images permettent de déterminer les contacts entre une personne ou un objet présent dans le sas et le sol ainsi que de calculer son poids et d'avoir une idée de la répartition de ce poids dans le plan. D'autre part, le tapis de pression est capable d'acquérir des images de pression de façon périodique ce qui permet également d'étudier le comportement dynamique de ces objets et d'en déduire, par exemple, une vitesse moyenne de déplacement, une direction ainsi que les déplacements relatifs entre objets. Le sas est également pourvu d'un second système de capteurs constitué des caméras vidéo 1.5 et 1.6. Ces caméras sont au nombre de deux dans l'exemple de réalisation, mais leur nombre peut être plus ou moins élevé en fonction de la quantité d'information que l'on souhaite obtenir. On peut, en particulier, ajouter une caméra sur le dessus. Ces caméras fournissent des images de profil 1.2, 1.3 permettant de déterminer des profils 1.8, 1.9 associés aux personnes ou objets présents dans le sas. Le sol et les parois du sas peuvent être de couleurs saturées afin de limiter les problèmes induits par les ombres portées par les personnes ou objets présents dans le sas. Un exemple d'image de profil est représenté Fig. 3.In the embodiment described, the invention is carried out within an airlock controlling an access. This airlock is shown schematically Fig. 1 . A person 1.1 crosses the airlock from left to right. The airlock is equipped with a number of sensor systems. We call sensor system, a system for the acquisition of information and based on a plurality of sensors of the same type. The airlock is equipped at ground level with a first sensor system consisting of a pressure sensitive mat 1.4. This carpet provides a two-dimensional pressure image 1.7 providing at each of its points the value of the pressure exerted. An example of these pressure images is shown Fig. 4 . These images make it possible to determine the contacts between a person or an object present in the airlock and the ground as well as to calculate its weight and to have an idea of the distribution of this weight in the plane. On the other hand, the pressure pad is able to acquire pressure images of periodically which also allows to study the dynamic behavior of these objects and to deduce, for example, an average speed of movement, a direction as well as the relative displacements between objects. The airlock is also provided with a second sensor system consisting of video cameras 1.5 and 1.6. These cameras are two in the embodiment, but their number may be higher or lower depending on the amount of information that is desired. One can, in particular, add a camera on the top. These cameras provide profile images 1.2, 1.3 to determine profiles 1.8, 1.9 associated with people or objects in the airlock. The floor and the walls of the airlock can be of saturated colors in order to limit the problems induced by the shadows carried by the persons or objects present in the airlock. An example of a profile image is shown Fig. 3 .

Ce dispositif peut être complété par d'autres systèmes de capteurs comme des barrières infrarouges, des diodes, des lasers ou autres permettant de détecter l'arrivée d'une personne ou d'un objet dans le sas, de mesurer la chaleur émise par une personne ainsi que tout autre paramètre utile. Le sas se voit, de plus, généralement, muni de moyens d'authentification non représentés comme un lecteur de badge ou des moyens d'identification biométriques comme un lecteur d'iris de l'oeil ou d'empreintes digitales.This device can be supplemented by other sensor systems such as infrared barriers, diodes, lasers or others to detect the arrival of a person or an object in the airlock, to measure the heat emitted by a person as well as any other useful parameter. The airlock is, moreover, generally provided with authentication means not shown as a badge reader or biometric identification means such as an iris reader of the eye or fingerprints.

Le sas est typiquement connecté à des moyens d'acquisition des données produites par les systèmes de capteurs, des moyens d'analyse de ces données, de prise de décision ainsi que de contrôle. Ces moyens peuvent être constitués d'un ordinateur 1.9 qui est doté d'un disque dur permettant le stockage des images reçues, tant de pression que de profils, ainsi que des programmes nécessaires pour traiter ces images et en extraire les paramètres qui sont utilisés pour déterminer si le passage est validé ou non. Dans le cas d'un passage valide, cet ordinateur peut, par exemple, autoriser l'ouverture d'une porte située à l'extrémité du sas. Dans le cas contraire, la porte reste fermée et une alarme peut être émise en direction d'un poste de surveillance ou autre.The airlock is typically connected to data acquisition means produced by the sensor systems, means for analyzing these data, decision-making and control. These means may consist of a computer 1.9 which is provided with a hard disk for storing received images, both pressure and profiles, as well as programs necessary to process these images and extract the parameters that are used to determine whether the passage is validated or not. In the case of a valid passage, this computer can, for example, allow the opening of a door located at the end of the lock. Otherwise, the door remains closed and an alarm can be sent to a monitoring station or other.

Une personne désireuse de frauder et donc d'entrer sans autorisation, tente généralement de profiter du passage d'une personne autorisée pour se faufiler par la porte via le sas. Cette tentative peut se faire à l'insu de la personne autorisée qui supposera, par exemple, que la personne la suivant est également autorisée. Cette tentative peut également se faire avec la complicité de la personne autorisée ou encore par coercition. Il s'agit donc pour le fraudeur de tenter de tromper les systèmes de capteurs en essayant de dissimuler son passage. Pour ce faire, il peut tenter de se coller à la première personne, par exemple dos à dos, pour tromper les caméras et de coller ses pieds à ceux de la première personne pour que le système ne distingue que deux « grandes » empreintes de pas, voir par exemple l'image de pression Fig. 6. Nous appellerons ce type de fraude « fraude collé ». Le fraudeur peut également tenter de passer accroupi, ou encore en restant exactement sur le côté de la personne autorisée. Certains cas particuliers peuvent également poser des problèmes de reconnaissance d'un enfant au côté d'un adulte ou même d'un bébé dans les bras de sa mère. Ces tentatives de fraude ne représentant que des exemples des types de fraude possibles. L'erijeu du système est donc de réussir à discriminer les passages valides d'une seule personne et ceci quels que soient la taille, la corpulence, la tenue ou les bagages de cette personne d'une tentative de fraude comme celles que nous venons de décrire.A person wishing to defraud and therefore enter without authorization, usually tries to take advantage of the passage of an authorized person to sneak through the door via the airlock. This attempt may be made without the knowledge of the authorized person assume, for example, that the next person is also authorized. This attempt can also be made with the complicity of the authorized person or by coercion. It is therefore for the fraudster to try to deceive the sensor systems trying to hide his passage. To do this, he can try to stick to the first person, for example back-to-back, to deceive the cameras and stick his feet to those of the first person so that the system only distinguishes two "large" footprints , see for example the pressure picture Fig. 6 . We will call this type of fraud "stuck fraud". The fraudster may also attempt to squat, or by staying exactly on the side of the authorized person. Some special cases may also cause problems in recognizing a child next to an adult or even a baby in the arms of his mother. These fraud attempts are only examples of possible types of fraud. The challenge of the system is therefore to succeed in discriminating the valid passages of a single person and this regardless of the size, the body, the dress or the luggage of this person of an attempt of fraud like those which we come from to describe.

En fonction de ces types de fraude que l'on doit détecter, il faut choisir un certain nombre de paramètres issus des systèmes de capteurs. Ces paramètres peuvent être des données directement issues des capteurs ou des paramètres calculés à partir des informations fournies.Depending on these types of fraud that must be detected, it is necessary to choose a certain number of parameters from the sensor systems. These parameters can be data directly derived from the sensors or parameters calculated from the information provided.

Pour le système de caméras, il est possible d'obtenir à partir des images prises, des images dites de profil. Ces images sont obtenues par discrimination du sujet par rapport au fond. Les techniques de traitement d'images numériques nécessaires sont connues. Une fois ces images de profils obtenues, il est possible d'en extraire des paramètres comme illustré par la Fig. 3. On obtient facilement l'emplacement du centre de gravité 3.3 de l'objet 3.2, sa hauteur 3.6, sa largeur 3.5. Par une analyse des images au cours du temps, il est également possible d'extraire la vitesse moyenne 3.4 du centre de gravité. Il est aussi possible d'appliquer un algorithme permettant de compter les têtes, en fait un algorithme qui va compter les excroissances du profil 5.1 dans sa partie haute. Par croisement des profils issus de plusieurs caméras, il est encore possible de calculer le volume de l'objet, ainsi que la répartition de ce volume en fonction de la hauteur de l'objet. On peut, par exemple, choisir de diviser la hauteur en trois parties égales et déterminer le pourcentage du volume situé dans la partie basse, la partie médiane et la partie haute de l'objet. Ces paramètres ne représentent que des exemples des paramètres envisageables issus du système de caméras.For the camera system, it is possible to obtain from the images taken, so-called profile images. These images are obtained by discriminating the subject from the background. The necessary digital image processing techniques are known. Once these images of profiles obtained, it is possible to extract parameters as illustrated by the Fig. 3 . The location of the center of gravity 3.3 of the object 3.2, its height 3.6, its width 3.5 is easily obtained. By analyzing the images over time, it is also possible to extract the average speed 3.4 from the center of gravity. It is also possible to apply an algorithm to count the heads, in fact an algorithm that will count the excrescences of the profile 5.1 in its upper part. By crossing profiles from several cameras, it is still possible to calculate the volume of the object, as well as the distribution of this volume according to the height of the object. One can, for example, choose to divide the height in three equal parts and determine the percentage of the volume located in the lower part, the middle part and the upper part of the object. These parameters are only examples of the possible parameters from the camera system.

De manière analogue, des paramètres sont extraits du système de capteurs constitué par le tapis de pression. Les images de pression, telles que celles illustrées Fig. 4, permettent ici aussi d'obtenir pour chaque objet 4.2, sa hauteur 4.6, sa largeur 4.5 et le centre de gravité global des objets détectés 4.3. Une étude de l'évolution au cours du temps des objets permet de calculer la vitesse de déplacement 4.4 moyenne de ce centre de gravité ainsi que la moyenne au cours du temps des valeurs précédentes. Il est également possible de calculer une hauteur et une largeur globales. Une intégration des valeurs de pression permet une estimation du poids total des objets présents dans le sas.Similarly, parameters are extracted from the sensor system constituted by the pressure belt. Pressure images, such as those illustrated Fig. 4 , here also make it possible to obtain for each object 4.2, its height 4.6, its width 4.5 and the overall center of gravity of the detected objects 4.3. A study of the evolution over time of the objects makes it possible to calculate the average displacement speed 4.4 of this center of gravity as well as the average over time of the previous values. It is also possible to calculate an overall height and width. An integration of the pressure values allows an estimation of the total weight of the objects present in the airlock.

On peut faire de même avec tous les systèmes de capteurs que l'on choisit d'utiliser. Chacun d'eux est susceptible de fournir des paramètres pouvant être utiles pour la discrimination des différents types de fraude possibles dans le sas.We can do the same with all the sensor systems that we choose to use. Each of them is likely to provide parameters that can be useful for the discrimination of different types of possible fraud in the airlock.

Outre ces paramètres issus de chaque système de capteurs, le fait d'utiliser au moins deux systèmes de capteurs rend possible le calcul de paramètres supplémentaires issus de la corrélation d'informations fournies par chacun des systèmes de capteurs. Il est par exemple possible d'établir un ratio volume/poids des objets présents dans le sas, ou encore la différence de vitesse de déplacement entre les objets détectés par les caméras et les objets détectés par le tapis de pression. Il est aussi possible de comparer les positions et le nombre de contacts au sol avec les objets détectés par les caméras.In addition to these parameters from each sensor system, the fact of using at least two sensor systems makes it possible to calculate additional parameters resulting from the correlation of information provided by each of the sensor systems. It is for example possible to establish a volume / weight ratio of the objects present in the airlock, or the difference in speed of movement between the objects detected by the cameras and the objects detected by the pressure belt. It is also possible to compare the positions and the number of ground contacts with the objects detected by the cameras.

Un choix est fait parmi tous ces paramètres possibles. On définit ainsi un certain nombre de jeu de paramètres comme illustré Fig. 6, étape 6.1. On fait correspondre les paramètres choisis issus d'un système de capteurs à un jeu de paramètres. Les paramètres issus de la corrélation entre deux systèmes de capteurs vont également fournir un jeu de paramètres. On obtient donc ainsi un jeu de paramètres par système de capteurs et un jeu de paramètres par corrélation faite entre deux systèmes de capteurs. Pour chaque accès par le sas, le système est donc capable de calculer un ensemble de jeux de valeurs pour chaque jeu de paramètres correspondant à cet accès.A choice is made among all these possible parameters. We thus define a certain number of parameter sets as illustrated Fig. 6 step 6.1. The selected parameters from a sensor system are matched to a set of parameters. The parameters resulting from the correlation between two sensor systems will also provide a set of parameters. Thus we obtain a set of parameters per sensor system and a set of parameters by correlation made between two systems of sensors. For each access through the airlock, the system is therefore able to calculate a set of sets of values for each set of parameters corresponding to this access.

Pour pouvoir déterminer la validité d'un accès, c'est-à-dire répondre à la question de savoir si ce passage correspond au passage d'une seule personne ou pas, il faut donc déterminer si un ensemble de jeux de paramètres calculés lors de cet accès correspond au passage d'une seule personne ou à une tentative de fraude.To be able to determine the validity of an access, that is to say to answer the question of whether this passage corresponds to the passage of a single person or not, it is necessary to determine if a set of sets of parameters calculated during this access corresponds to the passage of a single person or an attempt at fraud.

Pour ce faire, il est possible de procéder à une phase d'apprentissage. Les valeurs des différents jeux de paramètres définis plus haut vont être enregistrées. Chaque jeu de paramètres peut être vu comme un espace multidimensionnel où chaque dimension correspond à un paramètre. Lors d'un passage déterminé, les valeurs calculées pour chaque paramètre définissent un vecteur dans cet espace représentant le jeu de valeurs. Ceci est illustré Fig. 2. Sur cette figure, est représenté un espace de dimension trois correspondant à un jeu de trois paramètres. Chacune des dimensions 2.1, 2.2, 2.3 correspond donc à un paramètre du jeu. Le vecteur 2.3 correspond aux valeurs mesurées ou calculées lors d'un passage donné. Les mesures successives de différents passages donnent une collection de vecteurs définissant une classe de valeurs correspondant à ces passages. Une telle classe 2.5 est représentée Fig. 2. Pour chaque jeu de paramètres on définit ainsi une classe correspondant aux mesures effectuées lors d'une série de passages. Si l'on effectue de telles séries de mesures pour des passages valides, puis pour des passages correspondant à des tentatives de fraude on établit pour chaque jeu de paramètres des classes correspondant à un passage valide et des classes correspondant aux types de fraude envisagés. On obtient ainsi, comme illustré Fig. 6, étape 6.2, et pour chaque jeu de paramètres, une classe correspondant aux différentes tentatives de fraude.To do this, it is possible to proceed to a learning phase. The values of the different sets of parameters defined above will be saved. Each set of parameters can be viewed as a multidimensional space where each dimension corresponds to a parameter. During a determined passage, the calculated values for each parameter define a vector in this space representing the set of values. This is illustrated Fig. 2 . In this figure, is represented a space of dimension three corresponding to a set of three parameters. Each of the dimensions 2.1, 2.2, 2.3 thus corresponds to a parameter of the game. The vector 2.3 corresponds to the values measured or calculated during a given passage. The successive measurements of different passages give a collection of vectors defining a class of values corresponding to these passages. Such a class 2.5 is represented Fig. 2 . For each set of parameters, a class corresponding to the measurements made during a series of passages is thus defined. If such series of measurements are taken for valid passages and then for passages corresponding to fraud attempts, classes corresponding to a valid passage and classes corresponding to the types of fraud envisaged are established for each set of parameters. We obtain as illustrated Fig. 6 , step 6.2, and for each set of parameters, a class corresponding to the various fraud attempts.

Lorsque l'on cherche à classifier un passage ou accès on commence donc par acquérir les informations de chaque système de capteurs. Ces informations sont ensuite utilisées pour calculer les paramètres correspondant à chaque jeu de paramètres. On obtient donc les jeux de valeurs correspondant à chaque jeu de paramètres, comme illustré Fig. 6, étape 6.3. Il est donc possible de calculer une mesure de distance entre les valeurs de paramètres mesurées et/ou calculées d'un jeu de paramètres et les différentes classes correspondant aux différents types de passages. Cette mesure de distance peut être une simple distance algébrique entre le vecteur mesuré et le barycentre des vecteurs de la classe ou toute autre mesure de distance dans l'espace. On déduit de cette distance une probabilité que le passage appartienne à la classe considérée, comme illustré Fig. 6, étape 6.4. Chaque jeu de paramètres est ainsi classifié et une probabilité est associée à cette classification. La classification du passage s'effectue par consolidation des classifications obtenues pour chaque jeu de paramètres, comme illustré Fig. 6, étape 6.5.When one wants to classify a passage or access, one begins by acquiring the information of each sensor system. This information is then used to calculate the parameters corresponding to each set of parameters. We thus obtain the sets of values corresponding to each set of parameters, as illustrated Fig. 6 step 6.3. It is therefore possible to calculate a distance measurement between the values of measured and / or calculated parameters of a set of parameters and the different classes corresponding to the different types of passages. This measure of distance can be a simple algebraic distance between the measured vector and the centroid of the vectors of the class or any other measure of distance in space. We deduce from this distance a probability that the passage belongs to the class considered, as illustrated Fig. 6 step 6.4. Each set of parameters is thus classified and a probability is associated with this classification. The classification of the passage is carried out by consolidation of the classifications obtained for each set of parameters, as illustrated Fig. 6 step 6.5.

Alternativement, les étapes de classification d'un jeu de paramètres peuvent être effectuées par un réseau de neurones formels, autrement appelé réseau neuromimétique. Ces réseaux fonctionnent sur le modèle d'une interconnexion de neurones formels, chacun de ses neurones formels effectuant une somme pondérée de ses entrées et appliquant à cette somme une fonction de sortie non linéaire qui peut être un simple seuil ou une fonction plus sophistiquée comme la fonction sigmoïde. La connaissance ou l'information stockée dans le réseau correspond aux poids synaptiques de chaque neurone, ces poids étant calculés par apprentissage. Cet apprentissage se fait à l'aide d'un algorithme « d'entraînement » qui consiste à modifier les poids synaptiques en fonction d'un jeu de données présentées en entrée du réseau. Le but de cet entraînement est de permettre au réseau de neurones « d'apprendre » à partir des exemples. Si l'entraînement est correctement réalisé, le réseau est capable de fournir des réponses en sortie très proches des valeurs d'origines du jeu de données d'entraînement. Mais tout l'intérêt des réseaux de neurones réside dans leur capacité à généraliser à partir du jeu de test. Un tel réseau de neurones entraîné sur les passages constituant les classes lors d'une phase d'apprentissage est donc à même de réaliser avec fiabilité une classification des passages et de donner pour chaque passage une probabilité associée à chaque jeu de paramètres et à chaque passage ou accès.Alternatively, the classification steps of a set of parameters can be performed by a formal neural network, otherwise known as a neuromimetic network. These networks operate on the model of an interconnection of formal neurons, each of its formal neurons performing a weighted sum of its inputs and applying to this sum a nonlinear output function which may be a simple threshold or a more sophisticated function such as sigmoid function. The knowledge or information stored in the network corresponds to the synaptic weights of each neuron, these weights being calculated by learning. This learning is done using a "training" algorithm which consists in modifying the synaptic weights according to a data set presented at the input of the network. The purpose of this training is to allow the neural network to "learn" from the examples. If the drive is properly performed, the network is able to provide output responses very close to the original values of the training data set. But all the interest of neural networks lies in their ability to generalize from the test game. Such a network of neurons driven on the passages constituting the classes during a learning phase is thus able to reliably perform a classification of the passages and to give for each passage a probability associated with each set of parameters and each passage or access.

La pertinence du choix des paramètres constituant le jeu de paramètres pour chaque système de capteurs, l'utilisation de jeux de paramètres supplémentaires impliquant dans leur calculs plusieurs systèmes de capteurs ainsi que la caractérisation dans l'espace de chaque jeu de paramètres des types de fraude par apprentissage sont autant de facteurs contribuant chacun à la robustesse et à la fiabilité de la classification.The relevance of the choice of parameters constituting the set of parameters for each sensor system, the use of additional parameter sets involving in their calculations several sensor systems as well as the characterization in space of each set of parameters of the types of fraud learning are all contributing factors to the robustness and reliability of the classification.

L'homme du métier comprendra que l'invention, bien que décrivant l'utilisation d'un tapis de pression et de caméra, peut inclure de la même façon différents systèmes de capteurs comme des barrières infrarouges ou laser, des caméras infrarouges, des systèmes de diodes ou tout autre moyen d'obtenir des informations sur les objets ou corps présent dans un espace de contrôle. De même, l'invention décrite vise à discriminer l'unicité de présence d'une personne, mais elle pourrait tout aussi facilement s'appliquer à d'autres critères, comme l'unicité d'un véhicule ou autres.Those skilled in the art will understand that the invention, while describing the use of a pressure and camera belt, may likewise include different sensor systems such as infrared or laser barriers, infrared cameras, diodes or any other means of obtaining information about objects or bodies present in a control space. Similarly, the described invention aims to discriminate the uniqueness of a person's presence, but it could just as easily apply to other criteria, such as the uniqueness of a vehicle or others.

Claims (7)

  1. Method of protecting physical access having a plurality of sensor systems (1.4, 1.5, 1.6), the method being aimed at discriminating valid access from a fraudulent attempt at access, comprising the following steps:
    in a preliminary phase:
    - determining at least one set of parameters issuing from sensor systems including at least one set of parameters issuing from at least two different systems (6.1);
    - determining by learning, for each set of parameters and for each type of fraud envisaged, a class of values of the parameters in the set corresponding to this type of fraud for this set of parameters (6.2);
    during access:
    - determining sets of values formed by the values taken by each parameter of each set of parameters for this access (6.3);
    - determining a probability of fraud associated with each type of fraud for each set of parameters, according to the set of values determined during this access and the class corresponding to the type of fraud for this set of parameters (6.4);
    - determining a global probability of fraud associated with the access according to the probabilities of fraud obtained for each set of parameters and for each type of fraud (6.5).
  2. Method according to claim 1, where the probability of fraud associated with each type of fraud for each set of parameters is estimated by calculating a distance between the set of values determined during this access and the class corresponding to the type of fraud for this set of parameters.
  3. Method according to claim 2, where this distance is an algebraic distance between the set of values determined and the barycentre of the class.
  4. Method according to claim 1, where the probability of fraud associated with each type of fraud for each set of parameters is estimated by a neuromimetic network and where the step of determining the classes by learning comprises a step of training this neuromimetic network.
  5. Method according to any one of the preceding claims, where the sensor systems comprise a system of cameras (1.5, 1.6) supplying profile images (1.8, 1.9, fig 3).
  6. Method according to any one of the preceding claims, where the sensor systems comprise a pressure mat system on the ground (1.4) supplying pressure images (1.7, fig 4).
  7. Device for protecting a physical access comprising:
    - a control space;
    - a plurality of sensor systems in this control space (1.4, 1.5, 1.6)
    - means of analysing the information issuing from the sensor system (1.9) ;
    characterised in that, there being determined at least one set of parameters issuing from the sensor systems, including at least one set of parameters issuing from at least two different sensor systems, being determined by learning, for each set of parameters and for each type of fraud envisaged, a space class of values of the parameters of the set corresponding to this type of fraud for this set of parameters, the analysis means comprising:
    - means of determining sets of values formed from the values taken by each parameter of each set of parameters for this access;
    - means of determining a probability of fraud associated with each type of fraud and for each set of parameters, according to the set of values determined during this access and the class corresponding to the type of fraud for this set of parameters;
    - means of determining a global probability of fraud associated with the access according to the probabilities of fraud obtained for each set of parameters and for each type of fraud.
EP06829334A 2005-12-16 2006-12-06 Method of securing a physical access and access device implementing the method Not-in-force EP1960973B1 (en)

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FR0512857A FR2895122B1 (en) 2005-12-16 2005-12-16 METHOD OF SECURING PHYSICAL ACCESS AND PROVIDING ACCESS TO THE PROCESS
PCT/EP2006/011700 WO2007068385A1 (en) 2005-12-16 2006-12-06 Method of securing a physical access and access device implementing the method

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EP1960973A1 EP1960973A1 (en) 2008-08-27
EP1960973B1 true EP1960973B1 (en) 2011-10-12

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EP (1) EP1960973B1 (en)
CN (1) CN101385050B (en)
AT (1) ATE528735T1 (en)
AU (1) AU2006326345B2 (en)
BR (1) BRPI0619993B1 (en)
CA (1) CA2634228C (en)
ES (1) ES2372761T3 (en)
FR (1) FR2895122B1 (en)
MY (1) MY149945A (en)
PT (1) PT1960973E (en)
WO (1) WO2007068385A1 (en)
ZA (1) ZA200805553B (en)

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FR2895122A1 (en) 2007-06-22
AU2006326345B2 (en) 2012-03-08
CN101385050A (en) 2009-03-11
FR2895122B1 (en) 2008-02-01
BRPI0619993A2 (en) 2011-10-25
ATE528735T1 (en) 2011-10-15
US7847688B2 (en) 2010-12-07
CA2634228A1 (en) 2007-06-21
ZA200805553B (en) 2009-09-30
WO2007068385A1 (en) 2007-06-21
PT1960973E (en) 2011-12-19
CN101385050B (en) 2012-09-05
ES2372761T3 (en) 2012-01-26
CA2634228C (en) 2013-12-03
US20090002144A1 (en) 2009-01-01
AU2006326345A1 (en) 2007-06-21
BRPI0619993B1 (en) 2018-04-24
MY149945A (en) 2013-11-15
EP1960973A1 (en) 2008-08-27

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