Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR 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; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F18/00—Pattern recognition
  • G06F18/20—Analysing
  • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
  • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F18/00—Pattern recognition
  • G06F18/20—Analysing
  • G06F18/24—Classification techniques
  • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
  • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/40—Extraction of image or video features
  • G06V10/54—Extraction of image or video features relating to texture
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
  • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
  • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
  • G06V10/758—Involving statistics of pixels or of feature values, e.g. histogram matching
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/98—Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns
  • G06V10/993—Evaluation of the quality of the acquired pattern
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
  • G06V40/12—Fingerprints or palmprints
  • G06V40/1347—Preprocessing; Feature extraction
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
  • G06V40/12—Fingerprints or palmprints
  • G06V40/1365—Matching; Classification
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/40—Spoof detection, e.g. liveness detection
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/40—Extraction of image or video features
  • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
  • G06V10/467—Encoded features or binary features, e.g. local binary patterns [LBP]

Definitions

• the invention relates to a method for verifying whether a fingerprint presented during a check is a real fingerprint or a dummy fingerprint. It is used in particular to detect presentation attacks (false fingers) on fingerprint sensors.
• attack detection by presenting fingerprints means that we will detect whether a fingerprint presented on a suitable sensor is a real fingerprint or a dummy fingerprint, and thus avoid a fraudulent use of a person's identity.
• the fingerprint is one of the most widely used biometric methods to secure access and the issuance of sovereign titles. This massive use of fingerprints has led to the emergence and proliferation of attacks on biometric systems. For example, an individual previously expelled from a country can re-enter the territory by replacing the prints of his right hand with those of his left hand, at an access control gate or by using a finger dummy.
• attack detection system by presentation also called “anti-spoofing” in the prior art.
• the purpose of this system will be to generate an alert in the presence of a false finger, in order to avoid the issuance or the use of sovereign titles to impostors.
• solutions are proposed in the state of the art for the detection of attack by presentation. These solutions are of two types: the hardware approach and the software approach.
• the dynamic software approach consists of capturing multiple images of the fingerprint over a period of finger movement on the sensor, a rotation and a long press of the fingerprint lasting from zero to five seconds. These methods analyze the variations on several successive images. They have the disadvantage of being less precise and above all they require more time during the acquisition of the imprint, which can appeal to an impostor.
• the static software approach is to use a single image of the fingerprint to determine whether it is a real finger or a dummy finger. This is the most popular state-of-the-art approach. Only one image is needed and the acquisition time is thus reduced.
• the solutions known in the prior art consider a fingerprint image as any image on which we will apply methods of extracting conventional image texture descriptors before making a decision using a Previously trained “classifier or classifier”.
• the texture descriptors measure the local variations in intensity on each of the pixels of the image. The measurement of these variations, in a global way, gives the texture of the analyzed image.
• the texture descriptors are calculated for each of the pixels of the image and correspond to a redefinition of a pixel with respect to its local neighborhood.
• One of the best known descriptors is the “local binary pattern” known by the abbreviation LBP, acronym for “Local Binary Pattern”.
• the descriptors are then inserted into a classifier of the support vector machine type better known by the English abbreviation SVM (Support Vector Machine) or neural networks (NNET) which are models of machine learning by machine (or Machine Learning) to learn the factors discriminants on the descriptors.
• SVM Small Vector Machine
• NNET neural networks
• These models use notions of probability calculations to find the set of descriptors allowing the best possible separation between real fingerprints and dummy fingerprints.
• the idea of the present invention is to provide a new method for detecting attack by presentation which will use business descriptors, derived from knowledge of fingerprints, combined with conventional texture descriptors.
• business descriptors denotes descriptors which reflect the characteristics of a detail, which includes the global descriptor and the local descriptor of an imprint
• texture or LBP descriptors the descriptors associated with the texture of the image of the imprint.
• the minutiae are specific points of the imprint which materialize a particular deformation of a ridge and valley.
• the idea of the method according to the invention is based on the exploitation of business descriptors based on statistical estimators of characteristic elements of a fingerprint as well as on the quality of these indices.
• the method will use minutiae extractors for fingerprints which provide several exploitable information and which will help in the construction of discriminating descriptors to discriminate the real fingerprints and the dummy fingerprints.
• the object of the invention relates to a method for detecting an attack by presenting fingerprints comprising at least the following steps:
• a minutiae extractor and extract a number n of minutiae M n , a minutiae being characterized by at least its abscissa, x, its ordinate y, its type t, its orientation Q and its quality index q,
• the level of asymmetry of the values around the mean S Information on the flattening of the distribution of the variable w performed for all the minutiae extracted on each of the following variables: the type of minutiae, its orientation, its quality index, the distance separating said minutiae to at least one neighboring minutiae, the number of peaks separating said minutiae M j to said neighboring thoroughness considered,
• the method can further comprise the following steps:
• the method further comprises a step of calculating the overall variation of the directions of minutiae of the imprint by comparing the minutiae read with the variable DQ with the global directions of the imprint contained in a file. and adding the global variation value of the directions to the business descriptor vector V 2 .
• the method may further include a step of determining the reading error frequency f ( eri ect) on the directions of the minutiae and adding this value to the business descriptor vector V 2 .
• the method can also include a step of calculating the number of empty zones, N ( zv ) defined by the number of occurrences of a value equal to "1" where the value "1" indicates a zone read as empty of l 'fingerprint, and adding this value to the business descriptor vector V 2 .
• the statistical descriptors will be applied to the distance characteristic of a minutiae, considering the distance taken with respect to the three minutiae considered as the closest neighbors.
• the invention also relates to an attack detection system by presenting fingerprints comprising a fingerprint sensor connected to a texture extractor configured to generate a texture vector Vi, a concatenation device, a configured discrimination algorithm. to generate a detection model by attacks, and a comparison device, characterized in that it further comprises the following elements:
• a minutiae extraction module configured for:
• a module configured to concatenate the texture vector Vi with the vector V 2 , to form a vector V c containing the characteristics of the k variables described by the set of minutiae acquired for a given fingerprint
• - Said comparator being configured to compare a fingerprint to be verified with the attack detection model and decide whether the fingerprint is a real fingerprint or a dummy fingerprint.
• the minutiae extractor module is, for example, configured to determine at least one of the following values:
• the discrimination algorithm used is for example an automatic machine learning model of SVM type or of neural networks type.
• FIG.1 illustrates an example of an architecture allowing the implementation of the method according to the invention
• FIG.2 a representation table of an imprint by all the minutiae extracted with the variables characteristic of its local behavior
• FIG.3 an illustration of "holes" between the ridges and valleys of a footprint
• FIG.4 an example of a file bearing the quality indices by area of the image of a fingerprint
• FIG.5 a sequence of steps of the method according to the invention using a combination of texture descriptors and descriptors of descriptive business statistics in the same vector to differentiate dummy fingerprints from real fingerprints.
• the following example is given to detect whether a fingerprint acquired by a fingerprint reader is a dummy fingerprint or a real fingerprint.
• the method is based in particular on the concatenation of conventional texture descriptors with business descriptors based on statistics of the biometric data. This will advantageously improve the precision of the classifier and therefore the control of the "veracity" of an imprint, real imprint or dummy imprint.
• the process will use labeled imprints, that is to say imprints of which we know whether they are real or fictitious.
• the method will use for this construction, a sufficient number of fingerprints, in the sense usually used for the construction of databases.
• FIG. 1 illustrates an example of a system architecture according to the invention comprising a fingerprint sensor 10 connected to a processing module 20 of the data acquired by the fingerprint sensor.
• the processing module 20 comprises a first module 21, texture extractor, configured to determine image texture descriptors, a second module 22 configured to process the data of the acquired footprint, in order to define business descriptors complementary to the statistical descriptors as will be detailed below.
• This module 22 contains a minutiae extractor whose statistical indicators are used to produce business descriptors which will be combined by those skilled in the art with the texture descriptors.
• the fingerprint sensor will allow the taking of labeled fingerprints for the construction of the model G, during a first phase I of the process, then the capture of a fingerprint whose authenticity is to be verified, during a second phase, phase II of the process.
• the texture extractor 21 consists of descriptors with local binary patterns or LBP (Local Binary Pattern).
• LBP Local Binary Pattern
• the concatenated vector is subjected to a discriminating algorithm 24 in order to generate a model for verifying the veracity of an imprint 25, phase I of the process.
• the generated G model will be used to decide whether an imprint is a real imprint or a dummy imprint, phase II of the process.
• the system comprises a comparator 26 taking as input data from a fingerprint acquired on the fingerprint sensor 20 and the data of the model 25 to detect whether the captured fingerprint is a real fingerprint. or a dummy imprint.
• the result can be displayed on a screen of an enrollment station or the result of the comparison will generate an alarm signal at an access control gate in the event of identity theft.
• the detection system according to the invention can be implemented in the enrollment stations available in town hall to apply for a passport. These stations allow the capture of images of the ten fingerprints of the applicant. The image of each imprint can thus be processed by comparator 26. If the result of this comparator indicates that one of the imprints is dummy, then the town hall officer carrying out the enrollment will receive an alert in order to be able to interrupt the passport application process.
• Algorithm 24 is a classifier of SVM (Support Vector Machine) type or of neural networks or NNET type which are machine learning models for learning the discriminating factors on the descriptors. Any supervised learning technique algorithm intended to solve discrimination problems can be used. These models use notions of probability calculations to find the set of descriptors allowing the best possible separation between false fingerprints and real ones. These algorithms are known to those skilled in the art and will not be detailed.
• the method according to the invention “injects” at the input of these discriminating algorithms, the vector V c resulting from the concatenation of the texture vector Vi and the business vector V 2 .
• a footprint is comparable to an alternating surface of a set of ridges and valleys parallel to most regions in the footprint.
• the deformations between ridges and valleys form the minutiae which is the most stable representation used for comparison and identification of fingerprints.
• the minutiae represent local discontinuities and mark the positions where a ridge ends or branches off.
• On an imprint it is possible to detect between [1, 150] minutiae knowing that fourteen minutiae are generally sufficient to perform a comparison.
• a minutia m (x, y, t, Q, q, dst 1; nb_cr 1; dst 2 , dst_cr 2 , dst 3 , bd_cr 3 ) is characterized by its abscissa, its ordinate y, its type t, its orientation Q, the quality index q associated with the thoroughness on the impression.
• Two types of minutiae are used, ie, bifurcations and endings.
• the orientation of a minutia is the angle formed by the deviation of the ridge used to identify the minutia from the horizontal.
• the variables dsti, nb_cn represent respectively the distance which separates the minutiae from its closest neighbor and the number of peaks which separate them.
• the indices 2 and 3 in the dsh nb_cn notation represent the same measurements for the second and third closest neighboring minutiae.
• dsti, nb_cr h i being the “rank” of the closest neighboring minutia with respect to the concerned minutia.
• Descriptors related to thoroughness are conventional descriptors used in the field of fingerprint biometrics. They are known to those skilled in the art and will therefore not be detailed.
• an imprint / can be represented on the basis of the aforementioned variables characterizing a thoroughness, by considering three more closely related minutiae:
• Figure 2 is a local representation of a fingerprint with ten minutiae and the variables described above.
• four statistical indicators are used: the mean w, the standard deviation E (w), the "skewness” S, the kurtosis K.
• the mean represents the indicator of central tendency of a distribution
• the standard deviation indicates the fluctuation of different values around the mean value.
• Skewness indicates the level of skewness of values around the mean while kurtosis gives information on the flattening of the distribution.
• the method according to the invention will in particular use the following four statistical indicators, which it will apply selected descriptors w: mean for w,
• the method will consider all of the n minutiae Mi, ..., M n of the captured imprint, then each variable of a minutia (with the exception of x and y), t, Q, q , dsti, nb_cr 1 dst 2 , dst_cr 2 , dst 3 , bd_cr 3 .
• the method will calculate for each of these variables the value of the four aforementioned statistical estimators.
• the calculation generates a set of values for all of the n minutiae of the imprint and on each of the minutiae variables: which form the components of a business vector V 2 which will be concatenated with the texture vector Vi.
• the values V m are linked to the j variables which constitute the minutiae of the imprint.
• the method uses in particular the following business descriptors w:
• the overall quality Q g of an actual impression will be higher than the overall quality obtained by a dummy impression.
• the difficulty of positioning a finger evenly on the fingerprint sensor results in the appearance of small empty areas on the image of the fingerprint or "holes" in the image.
• a dummy fingerprint image typically has more "blank areas", 30, shown in Figure 3, than a fingerprint image obtained with an actual fingerprint.
• the method can add additional business descriptors making it possible to better differentiate a real footprint and a dummy footprint, to improve and make the decision-making more reliable.
• the method can use:
• the quality frequency equal to zero on the overall quality ( Figure 4) - f (Q 0 ), The quality frequency equal to one on the overall quality - f (Qi),
• the quality frequency equal to two on the overall quality - f (Q 2 ),
• the quality frequency equal to three on the overall quality - f (Q 3 ),
• the quality frequency equal to four on the overall quality - f ( ⁇ 4)
• the quality frequency equal to five on the overall quality - f (Qs),
• the overall total quality of the imprint Q tg which corresponds to the sum of the qualities obtained for all the frequencies f (Q 0 ), f (Qi), f (Q 2 ), f ( ⁇ 3), f ( ⁇ 4), f (Qs),
• N The number of empty areas or holes present on an imprint
• Reading a file which contains the overall directions of the imprint (peak and valley directions) and reading this file gives a variable of directions complementary to those read specifically on the minutiae with the DQ variable.
• the output of the minutiae extractor 22 generates several files:
• the quality will be read in this file, an example of which is illustrated in FIG. 4.
• This file contains quality values varying from 0 to 5 per zone of the image, the value of 5 being given by way of illustration.
• the overall quality of a print image is extracted by adding up all the quality values per area of the image. Then then, for each of the values between 0 and 5, the number of occurrences is counted which represents the frequency of occurrence for each quality value.
• the quality frequency equal to 2 indicates the total number of occurrences of "2" found in the file F qm ;
• a Fi fm file which contains values "0" and "1" is representative of a map of the empty areas of the footprint or Low Flow Map. This file contains values of 0 and 1 only where 1 indicates an area read as empty in the footprint. By counting the number of occurrences of 1, we obtain the total number of empty areas of the imprint;
• An F hC m file representative of a high curvature map or High Curvature Map. This file makes it possible to count other additional singular points of the indentations called the “delta”, center and loop.
• the .hem file contains 0s and 1s with the frequency of 1s which indicates the presence of singular points on the imprint;
• the value 1 indicates the zones of strong contrasts indicating the presence of finger.
• the file F d m file contains information on the direction of the ridges and valleys of the footprint. The global variation of the directions on the footprint, then the associated reading errors are read in the .dm file which stands for Direction Map.
• This file contains values from -1 to 15. The value "-1" indicates an inability to read the direction of the imprint.
• the method will count in the files F
• FIG. 5 illustrates a succession of steps implemented by the method according to the invention.
• the first step 51 consists in acquiring several labeled fingerprints as defined above,
• an algorithm will extract a first texture vector, according to a known method such as LBPs for example.
• the second module will extract statistical descriptors from the local and global information supplied by a minutiae extractor in order to generate indicators allowing the construction of a business vector (local and global),
• a fifth step 55 the method executes an SVM-type algorithm for learning a decision model which will make it possible to discriminate a real fingerprint and a dummy fingerprint.
• the model will use a database containing several real footprints and several dummy footprints. For each of these imprints, steps 51, 52, 53 and 54 are carried out then at 55, all these extractions are automatically learned by the SVM which produces the separating model of a real imprint from a dummy imprint.
• the sixth step 56 generates a model which will be used to perform a comparison, in a seventh step 57, with acquired fingerprints in order to determine whether they are dummy fingerprints or real fingerprints.
• phase II the method will capture an imprint to be verified, i.e., which we seek to verify if it is a real or artificial imprint.
• the imprint is subjected to the model generated by the steps explained above, using a comparison technique known to those skilled in the art.
• step 53 Detailed description of step 53
• Step 53 for constructing the business vector comprises at least the following steps.
• step 53 of constructing the business vector can add to the business descriptor vector V 2 one or more of the following values:
• the overall total quality of the print Q tg the quality frequency equal to zero over the overall quality f (Q 0 ), or equal to one f (Qi), or equal to two f (Q 2 ), or equal to three f ( ⁇ 3 ⁇ 4), or equal to four f (C), or equal to five f (Q 5 ), the global variation of the directions of minutiae on the recorded imprint, the frequency of reading error on the directions of the minutiae, f ( er iect), the number of empty zones or holes present on an imprint, N ( zv ).
• the descriptor vector thus formed will be concatenated with the texture descriptor vector before being transmitted to the learning algorithm to generate an attack detection model by presenting fingerprints.
• the method will generate a business vector which will be concatenated with the texture vector.
• the method considers the following parameters:
• the information from a thoroughness extractor and the use of descriptors based on the estimators of descriptive statistics of mean, variance, skewness, kurtosis make it possible in particular to obtain more precision for validating or rejecting a fingerprint. as a real fingerprint or a dummy fingerprint, a quick check that can be used in real-time verification systems.

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