Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
  • G06V40/12—Fingerprints or palmprints
  • G06V40/1382—Detecting the live character of the finger, i.e. distinguishing from a fake or cadaver finger
  • G06V40/1388—Detecting the live character of the finger, i.e. distinguishing from a fake or cadaver finger using image processing
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
  • G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
  • G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
  • G06K7/12—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation using a selected wavelength, e.g. to sense red marks and ignore blue marks
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
  • H04N23/20—Cameras or camera modules comprising electronic image sensors; Control thereof for generating image signals from infrared radiation only
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
  • G06V40/14—Vascular patterns

Definitions

• the present invention relates to a method of detecting a venous network on a body part of an individual for the purpose of identifying said individual.
• the invention also relates to a method of biometric recognition of an individual.
• Automatic biometric recognition methods are increasingly used for the identification of individuals for example in the context of border control, access control to secure areas such as airport boarding areas or others, or data access controls.
• a biometric recognition method generally comprises the following steps:
• a biometric recognition method is implemented by means of an automated biometric recognition system comprising biometric signature capture means forming a signature, biometric signature storage means forming signatures and means for comparing captured biometric characteristics memorized biometric characteristics.
• the capture means are often image capture means.
• the storage or storage means are for example a memory (integrated circuit, barcode or other) which is embedded in a passport for storing biometric characteristics of the passport holder or a memory of a computer unit containing a database associating the biometric characteristics of individuals with identification data of these individuals.
• the comparison means are computer units incorporating calculation means for executing algorithms for processing and comparing the biometric characteristics.
• the biometric characteristics of the hand and more particularly the fingers of individuals are very often used for the implementation of these methods. It is thus known to perform a biometric recognition from fingerprints or venous networks of the fingers.
• the capture means consist of an image sensor.
• biometric characteristics of an enlisted person that is to say a duly identified person whose biometric characteristics have been memorized to allow recognition by the system.
• This representation may take the form of a fake finger provided with biometric characteristics or a simple image of a venous network.
• Automated biometric recognition systems are thus provided with fraud detection means for this purpose.
• These means generally of the hardware type, aim, for example, to detect fraud by:
• biometric recognition algorithms are arranged only to extract digital signatures from a medium, whatever its nature, and compare them to stored biometric signatures.
• fraud detection algorithms are arranged to determine the nature of the support presented without extracting the biometric signature. This is because the characteristics that will reveal the living character of the medium and those that will allow recognition are not the same or are not exploited in the same way.
• An object of the invention is to provide a simple means for detecting fraud.
• a method of detecting a venous network in a living human body portion comprising the steps of:
• the method comprises a configuration phase and a detection phase.
• the configuration phase includes the steps of:
• the detection phase comprises the steps of:
• the mode of implementation is particularly effective. It is also relatively simple: the detection of fraud is carried out without taking into account geometric information (position, orientation) relative to the gradient, but only the average values of the amplitudes of the gradients. As a result, the comparison is made on a limited number of data, and is therefore time consuming and resource intensive.
• the directional gradients are not directly compared to directional gradients stored in relation to particular users, but mean energies from these directional gradients are compared with thresholds. This is more an observation than a comparison, in the sense of "matching", as usually used in biometric recognition.
• the invention also relates to a biometric recognition method comprising a phase of detecting the reality of a human body part and a biometric recognition phase comprising the following steps:
• the detection phase being performed by the implementation of the detection method according to the invention and the recognition phase being performed only if the human body part is determined as real at the end of the detection phase.
• the recognition phase is started only if the detection phase is successful.
• the invention relates to a method for detecting a venous network in a human body part, here the hand and more particularly a finger 1 of the hand.
• the method is implemented by means of an automated system comprising a detection device 10, known per se, comprising an infrared illuminator 11 and an infrared image sensor 12 which are arranged opposite a positioning area finger 1 of the user.
• the positioning area is at the Once in the beam of the infrared illuminator 11 and in the field of the infrared image sensor 12.
• the positioning area can be materialized by a window against which the finger 1 can be applied and behind which are mounted the infrared illuminator 11 and the infrared image sensor 12, or the positioning area may be materialized by guides allowing the user to place his finger 1 in the beam of the infrared illuminator 11 and in the field of the image sensor infrared 12.
• the infrared frequency is chosen because it is strongly absorbed by hemoglobin and weakly absorbed by the tissues surrounding the venous network in a part of the human body: the venous network therefore exits with respect to the tissues surrounding it.
• the infrared illuminator 11 and the infrared image sensor 12 are connected to a computer processing unit 20 known per se.
• the processing computer unit 20 comprises a memory 21 containing a database of biometric characteristics, a program for managing the operation of the system, a program for processing the captured images to extract biometric characteristics and a comparison program for the biometric characteristics. extracted to the biometric characteristics of the database.
• these different programs are portions of a single program arranged to implement the method of the invention.
• the computing processing unit 20 also comprises a computing unit 22 arranged to execute the program in question and to control the entire system.
• the computer processing unit 20 also includes an input / output console allowing an operator to intervene on the system.
• the method of the invention comprises the steps of:
• the method of the invention comprises a configuration phase and a detection phase.
• the configuration phase includes the steps of:
• representations of venous network such as those that could be used by a fraudster.
• These representations may have the form of flat images, images wound on a cylindrical support, artificial reproductions of a human finger ...
• the detection phase comprises the steps of: capturing at least one infrared image of a venous network;
• the determination of the directional gradients comprises the steps of:
• the gradient is lower than in a direction transverse to the direction of the vein.
• the estimation of the direction of the gradients is preferably carried out by a maximization of the directional derivatives according to four directions (we retain the largest amplitude among the four directions of derivation).
• the finger In an infrared image of venous network captured on a real finger, the finger is not uniform in that the image has darker areas. In the center of the finger, the inter-phalanges junction diffusing more in the infrared makes it possible to accentuate the luminosity, and thus to accentuate the contrast between these zones and the rest of the finger. Contrast analysis provides information on both the pattern of the venous network and the non-uniformity of the image. Performing a contrast analysis in two different resolutions makes it possible to increase the number of information obtained both on the venous network and on the nonuniformity of the light scattering in the real finger. The higher this number of information, the more reliable the discrimination between the image of a real venous network and the image of a representation of a venous network.
• the second number of pixels (that of the largest blocks) is preferably equal to the square of the first number of pixels (that of the smaller blocks).
• the first number of pixels is advantageously equal to 9 (well suited for the detection of high contrast veins) and the second number of pixels is advantageously equal to 81 (well suited for the detection of large veins and low contrast).
• each block is shifted by one pixel relative to its neighbors.
• Directional gradients are determined in at least four directions (0 °, 90 °, 180 °, 270 °) by one of the following methods:
• the comparison is preferably made by firstly determining vectors from the mean energies of the directional gradients.
• an SVM Small Vector Machine
• the separation surface is representative of the threshold values for discriminating venous network images captured directly on human fingers and venous network images captured on venous network representations.
• the average energy vectors of the captured image are projected onto the separation surface so as to determine according to a sign of the projection if the image has been captured directly on a human finger or on a venous network representation.
• the determination of gradients directionals can be performed differently as described above.
• the number of pixels of the blocks may be different and for example be even (in this case the determination of the directional gradients will require to carry out in a known manner in itself an interpolation).
• Directional gradients can be calculated for only one resolution of the image.

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• Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• Multimedia (AREA)
• General Physics & Mathematics (AREA)
• Theoretical Computer Science (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Signal Processing (AREA)
• Human Computer Interaction (AREA)
• General Health & Medical Sciences (AREA)
• Artificial Intelligence (AREA)
• Toxicology (AREA)
• Electromagnetism (AREA)
• Health & Medical Sciences (AREA)
• Collating Specific Patterns (AREA)
• Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

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Citations (2)

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• 2012
  • 2012-09-28 FR FR1259239A patent/FR2996331B1/fr active Active
• 2013
  • 2013-09-27 JP JP2015533615A patent/JP6181763B2/ja active Active
  • 2013-09-27 EP EP13766573.3A patent/EP2901366A1/de not_active Withdrawn
  • 2013-09-27 US US14/431,547 patent/US9460335B2/en active Active
  • 2013-09-27 WO PCT/EP2013/070272 patent/WO2014049149A1/fr not_active Ceased
  • 2013-09-27 BR BR112015006933-9A patent/BR112015006933B1/pt not_active IP Right Cessation

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