EP1960973B1 - Verfahren zum sichern eines physischen zugangs und das verfahren implementierende zugangseinrichtung - Google Patents

Verfahren zum sichern eines physischen zugangs und das verfahren implementierende zugangseinrichtung 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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Prior art keywords
parameters
fraud
type
access
values
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Not-in-force
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EP06829334A
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English (en)
French (fr)
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EP1960973A1 (de
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)
  • Closed-Circuit Television Systems (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
  • Collating Specific Patterns (AREA)

Claims (7)

  1. Verfahren zur Sicherung eines physischen Zugangs, der über eine Vielzahl von Erfassungssystemen (1.4, 1.5, 1.6) verfügt, wobei das Verfahren darauf abzielt, einen rechtmäßigen Zugang von einem versuchten unrechtmäßigen Zugang zu unterscheiden, und die folgenden Schritte umfasst:
    in einer einleitenden Phase:
    - Bestimmung mindestens eines Satzes von Parametern, die von den Erfassungssystemen stammen, wobei mindestens ein Satz von Parametern aus wenigstens zwei verschiedenen Erfassungssystemen (6.1) stammt;
    - Bestimmung einer Werteklasse der Parameter des Satzes, der diesem Betrugstyp für diesen Parametersatz (6.2) entspricht, für jeden Parametersatz und für jeden in Betracht gezogenen Betrugstyp mittels Anlernen;
    während eines Zugriffs:
    - Bestimmung von Wertesätzen, die aus Werten gebildet werden, die für jeden Parameter jedes Parametersatzes für diesen Zugang (6.3) abgenommen wurden;
    - Bestimmung einer Betrugswahrscheinlichkeit, die jedem Betrugstyp für jeden Parametersatz zugeordnet ist, abhängig vom Wertesatz, der während dieses Zugangs bestimmt wird, und von der Klasse, die dem Betrugstyp für diesen Parametersatz (6.4) entspricht;
    - Bestimmung einer globalen Betrugswahrscheinlichkeit, die dem Zugang in Abhängigkeit von den Betrugswahrscheinlichkeiten zugeordnet ist, die für jeden Parametersatz und jeden Betrugstyp (6.5) erhalten wurden.
  2. Verfahren nach Anspruch 1, wobei die Betrugswahrscheinlichkeit, die jedem Betrugstyp für jeden Parametersatz zugeordnet ist, durch Berechnung eines Abstands zwischen dem Wertesatz, der während dieses Zugangs ermittelt wurde, und der Klasse, die dem Betrugstyp für diesen Parametersatz entspricht, geschätzt wird.
  3. Verfahren nach Anspruch 2, wobei dieser Abstand ein algebraischer Abstand zwischen dem ermittelten Wertesatz und dem Baryzentrum der Klasse ist.
  4. Verfahren nach Anspruch 1, wobei die Betrugswahrscheinlichkeit, die jedem Betrugstyp für jeden Parametersatz zugeordnet ist, über ein neuromimetisches Netz geschätzt wird, und wobei der Ermittlungsschritt mittels Anlernen der Klassen einen Schulungsschritt dieses neuromimetischen Netzes umfasst.
  5. Verfahren nach einem der vorhergehenden Ansprüche, wobei die Erfassungssysteme ein System mit Kameras (1.5, 1.6) umfassen, die Profilbilder (1.8, 1.9, Fig. 3) liefern.
  6. Verfahren nach einem der vorhergehenden Ansprüche, wobei die Erfassungssysteme ein druckempfindliches Mattensystem am Boden (1.4) umfassen, das Druckbilder (1.7, Fig. 4) liefert.
  7. Vorrichtung zur Sicherung eines physischen Zugangs, umfassend:
    - einen Kontrollraum;
    - eine Vielzahl von Erfassungssystemen in diesem Kontrollraum (1.4, 1.5, 1.6);
    - Mittel zum Analysieren der Informationen, die von den Erfassungssystemen stammen (1.9);
    wobei in diesem Zusammenhang festzuhalten ist, dass mindestens ein Satz von Parametern bestimmt wird, die aus den Erfassungssystemen stammen, wovon mindestens ein Satz von Parametern, die aus mindestens zwei verschiedenen Erfassungssystemen stammen, für jeden Parametersatz und für jeden in Betracht gezogenen Betrugstyp mittels Anlernen bestimmt wird, wobei eine Werteraumklasse der Parameter des Satzes dem Betrugstyp für diesen Parametersatz entspricht, wobei die Analysemittel umfassen:
    - Mittel zur Bestimmung von Wertesätzen, die aus Werten gebildet werden, die über jeden Parameter jedes Parametersatzes für diesen Zugang abgenommen wurden;
    - Mittel zur Bestimmung einer Betrugswahrscheinlichkeit, die jedem Betrugstyp und für jeden Parametersatz zugeordnet ist, abhängig vom Wertesatz, der während dieses Zugangs bestimmt wird, und von der Klasse, die dem Betrugstyp für diesen Parametersatz entspricht;
    - Mittel zur Bestimmung einer globalen Betrugswahrscheinlichkeit, die dem Zugang in Abhängigkeit von den Betrugswahrscheinlichkeiten zugeordnet ist, die für jeden Parametersatz und für jeden Betrugstyp erhalten wurden.
EP06829334A 2005-12-16 2006-12-06 Verfahren zum sichern eines physischen zugangs und das verfahren implementierende zugangseinrichtung Not-in-force EP1960973B1 (de)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
FR0512857A FR2895122B1 (fr) 2005-12-16 2005-12-16 Procede de securisation d'un acces physique et dispositf d'acces implementant le procede
PCT/EP2006/011700 WO2007068385A1 (fr) 2005-12-16 2006-12-06 Procede de securisation d’un acces physique et dispositif d’acces implementant le procede

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

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US (1) US7847688B2 (de)
EP (1) EP1960973B1 (de)
CN (1) CN101385050B (de)
AT (1) ATE528735T1 (de)
AU (1) AU2006326345B2 (de)
BR (1) BRPI0619993B1 (de)
CA (1) CA2634228C (de)
ES (1) ES2372761T3 (de)
FR (1) FR2895122B1 (de)
MY (1) MY149945A (de)
PT (1) PT1960973E (de)
WO (1) WO2007068385A1 (de)
ZA (1) ZA200805553B (de)

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

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