CN107103266B - The training of two-dimension human face fraud detection classifier and face fraud detection method - Google Patents

The training of two-dimension human face fraud detection classifier and face fraud detection method Download PDF

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CN107103266B
CN107103266B CN201610098933.8A CN201610098933A CN107103266B CN 107103266 B CN107103266 B CN 107103266B CN 201610098933 A CN201610098933 A CN 201610098933A CN 107103266 B CN107103266 B CN 107103266B
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facial image
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CN107103266A (en
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李松斌
袁海聪
邓浩江
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Nanhai Research Station Institute Of Acoustics Chinese Academy Of Sciences
Institute of Acoustics CAS
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Abstract

The present invention provides the generation methods of two-dimension human face fraud detection model, which comprises and first, all people's face picture in training set is pre-processed, normalization facial image is obtained；Second, LBP feature vector, Gabor wavelet feature vector sum one-dimensional pixel feature vector are extracted from each normalization facial image；Third carries out these three feature vectors to be spliced to form final feature vector；4th, using support vector machines to being trained based on being spliced to form final feature vector, obtain two-dimension human face fraud detection classifier；This method is extracted the characteristic information of the difference of face and photo；Feature extraction is simple and efficient, and is not needed user and is purposely cooperated, and good effect can be obtained in the case where low resolution.In addition, being based on two-dimension human face fraud detection classifier obtained by the above method, the invention also provides face fraud detection method, this method has the advantages that detection accuracy is high, can effectively prevent face fraud.

Description

The training of two-dimension human face fraud detection classifier and face fraud detection method
Technical field
The present invention relates to computer visions and graph and image processing field, in particular to two-dimension human face fraud detection classifier Training and face fraud detection method.
Background technique
Currently, two-dimentional biological identification technology (identification i.e. based on two-dimension human face biological characteristic) is a critically important research Field.View transformation, block, complicated outdoor light be always recognition of face difficult point.This is solved although doing a lot of work A little problems, but the fraud of face identification system attack loophole is but ignored by big multisystem.Face identification system relies on plane Figure carries out identity detection, and system is highly susceptible to the fraud attack of photograph print or electronic photo.For example, association, Asus With the laptop of the Windows XP and Vista of Toshiba all built-in IP Camera and biological recognition system, by sweeping Retouch the face certification user of user.(world-leading technical security meeting), the peace of Ha Noi university in black cap conference in 2009 How easily full loophole research group illustrates is out-tricked the face identification system of notebook using the human face photo of legitimate user (the Veriface III of association, the SmartLogon V1.0.0005 of Asus, the Face Recognition of Toshiba 2.0.2.32) enter notebook, and the use of these systems is all highest security level.This loophole is now by state, the U.S. Family's standard and Institute for Research and Technology include into national vulnerability scan.Therefore fraud attack is solved the problems, such as to improve face identification system Safety and robustness and to be applied to it be a very urgent demand in practice.
Fraud attack refers to that a people attempts to disguise oneself as other people by data falsification to obtain illegal access.Such as One people cheats face identification system by the photo of a legitimate user, video, mask, 3D model before camera.Although One people can also use other fraud modes such as makeup, lift face, but photo attack is the most common fraud attack, because of human face photo Can easily by download on the net, acquisition of taking on the sly.
Currently, fraud attack loophole has attracted more and more people's note that 2011 have also specially held one " IJCB2011 two-dimension human face cheats attack defending contest ".Although the research in this field is more and more and issues successively The database of some public openings, but the standard database of relatively objective exploitation test can be provided for fraud detection algorithm simultaneously It is not very much, this field or not mature enough, up to the present about optimal fraud detection algorithm, there is no unified to be total to Know.Current existing face fraud detection research method or too complicated, without practicability (it is practical use in require place real-time, quickly Reason) or some unconventional imaging systems (multiple spectra imaging) and high-resolution camera are used, these do not have reality The condition of application.
Summary of the invention
It is an object of the invention to overcome drawbacks described above existing for current two-dimension human face fraud detection method, pass through finder The subtle difference of face and corresponding photo, and a feature space is designed to protrude the difference.In fact, human face photo all contains one Determine the printing quality defect of degree, can detected well with texture.It is anti-by picture quality, printed matter feature, light The inspiration of difference is penetrated, the present invention proposes a kind of training method of two-dimension human face fraud detection classifier.This method is by from picture Middle extraction LBP, Gabor wavelet, corresponding feature vector in three kinds of feature spaces of pixel characteristic, then these three feature vectors are closed At a final feature vector, this feature vector is finally sent to a nonlinear SVM classifier and carries out classification based training Obtain a two-dimension human face fraud detection classifier.Based on the two-dimension human face fraud detection classifier, the present invention also provides one Kind face fraud detection method, it can be determined that going out input picture is face or fraud image.
To achieve the goals above, the present invention provides the training method of two-dimension human face fraud detection classifier, the sides Method includes: first, is pre-processed to all people's face picture in training set, and normalization facial image is obtained；Second, from each It normalizes and extracts LBP feature vector, Gabor wavelet feature vector sum one-dimensional pixel feature vector in facial image；Third, by this Three feature vectors carry out being spliced to form final feature vector；4th, using support vector machines to being spliced to form final spy Sign vector is trained, and obtains two-dimension human face fraud detection classifier.
In above-mentioned technical proposal, the method is specifically included:
Step S1) i-th of face picture in training set is pre-processed, wherein 1≤i≤L；Obtain 64 × 64 pixels The normalization facial image z of sizei；
Step S2) from normalization facial image ziMiddle extraction LBP feature vector L (zi)；
Step S3) from normalization facial image ziMiddle extraction Gabor wavelet feature vector G (zi)；
Step S4) facial image z will be normalizediIt zooms to 8 × 8 sizes, then by two dimensional image thaumatropy is one-dimensional picture Plain feature vector P (zi)；
Step S5) by step S2), step S3), step S4) extract three kinds of textural characteristics be spliced into final feature to Measure D (zi)=(L (zi),G(zi),P(zi))；
Step S6) based on Support vector regression algorithm to all feature vector D (zi) be trained, obtain two-dimentional people Face fraud detection classifier, wherein 1≤i≤L.
In above-mentioned technical proposal, the step S1) it specifically includes:
Step S1-1) image gray processing processing is carried out to face picture:
Face face picture is traversed, each pixel is handled, the rgb value of each pixel is obtained, passes through operation Red, blue, green value is extracted respectively, the gray value after calculating each pixel conversion:
Grey=(9798R+19235G+3735B)/32768
Wherein, Grey represents the gray value after conversion, and R, G, B respectively represent the red point of each pixel in image Amount, green component and blue component；
Step S1-2) gray level image size is adjusted to 64 × 64 using bilinear interpolation；
Step S1-3) image after being sized is enhanced:
The modification that image histogram is carried out using the statistical data of histogram, is gone out by adjusting each grey level pixel of image Existing probability is equal to change the pixel value of each grey level in image, to realize image enhancement；
Step S1-4) to enhanced image zooming-out image pixel matrix, obtain normalization facial image zi。
In above-mentioned technical proposal, the step S2) it specifically includes:
Step S2-1) to normalization facial image ziWithOperator obtains LBP image, then is by LBP image segmentation 3 × 3 overlapping regions, the statistic histogram for extracting 59 dimensions from each region respectively synthesize the statistic histogram of one 531 dimension Feature vector；
Step S2-2) to normalization facial image ziWithOperator, extraction obtain the statistic histogram feature of 59 dimensions Vector；
Step S2-3) to normalization facial image ziWithOperator extracts and obtains the statistic histogram spy of 243 dimensions Levy vector；
Step S2-4) feature vector that obtains step S2-1), step S2-2), step S2-3) synthesize a feature to Measure L (zi), the dimension of this feature vector is 59*9+59+243=833 dimension.
In above-mentioned technical proposal, the step S3) it specifically includes:
Step S3-1) facial image z will be normalizedi32 × 32 sizes are zoomed to, it is small to carry out Gabor to the image after scaling Wave conversion:
The Gabor filter in p different directions and q different scale is taken to handle image after scaling, each picture Vegetarian refreshments t0It can obtain p × q Gabor amplitude Characteristics, p × q Gabor amplitude Characteristics cascade up a referred to as Jet, write a Chinese character in simplified form For J, then pixel t in image0Jet are as follows:
J(t0)=(M0,0(t0),...,M0,7(t0),...,M4,0(t0),...,M4,7(t0))
The Gabor amplitude Characteristics of all pixels point are cascaded up to obtain the feature vector F (z of facial imagei):
F(zi)={ J (t0):t0∈zi}
Step S3-2) determine step S3-1) obtained feature vector F (zi) dimensionality reduction dimension, be based on Principal Component Analysis To feature vector F (zi) carry out dimensionality reduction, the Gabor wavelet feature vector G (z after obtaining dimensionality reductioni)。
In above-mentioned technical proposal, the step S3-2) it specifically includes:
Step S3-2-1) the feature vector F (z that obtains step S3-1)i) dimension d be divided into n equal part, determine new dimension Spend the value range of d'；
Original feature vector F (zi) dimension be d, the value of d is divided into n equal portions, value set is as follows:
Wherein,Indicate integer rounding operation；
If the feature that obtains after dimensionality reduction is G (zi), then its dimension d' takes this n value respectively:
Step S3-2-2) d' successively takes each value in set, calculate the corresponding face fraud inspection of all pictures of training set Survey mean absolute error set { MAEm}；
To the L picture in training set, calculate when d' takes each value in set, the corresponding people of all pictures of training set Face fraud detection mean absolute error is as follows:
Wherein, j indicates that jth picture in training set, k indicate that d' takes k-th of value in set, i.e.,lj Indicate the corresponding class label of jth picture in training set: 0 indicates fraud image, and 1 indicates real human face image,Indicate training Concentrate the classification estimated value of jth picture；It may finally obtain different MAE value set { MAEmWherein m ∈ 1,2 ..., n；
Step S3-2-3) take set { MAEmIn minimum M AEmin, with MAEminCorresponding d' is tieed up as final dimensionality reduction Degree；
Step S3-2-4) it is based on step S3-2-3) obtained d', using Principal Component Analysis to feature vector F (zi) into Row dimensionality reduction, the Gabor wavelet feature vector G (z after obtaining dimensionality reductioni)。
In above-mentioned technical proposal, the step S6) it specifically includes:
Step S6-1) based on Support vector regression algorithm building optimization problem；
Hypothesized model training set sample is { x(i),y(i)(i=1,2 ..., L), x(i)Indicate normalization facial image zi's Feature vector D (zi), y(i)Indicate the corresponding classification of the image: facial image or fraud image；Assuming that sample dimension is N, thenThe target of Support vector regression algorithm is to solve for two-dimension human face fraud detection classifier f (x), makes f (x(i)) and y(i)Between difference be not more than ε, ε is threshold value, controls the worst error between practical label value and predictive estimation value；So, f (x) it is defined as follows:
Wherein, " " indicates inner product of vectors；W and b is the parameter solved；
Step S6-2) optimization problem (1) is converted using method of Lagrange multipliers, it is converted to its antithesis Problem is solved, and the expression formula of two-dimension human face fraud detection classifier f (x) is obtained.
Based on the two-dimension human face fraud detection classifier that above method training obtains, the present invention also provides face frauds to examine Survey method, this method comprises:
Step T1) face picture to be detected is pre-processed, obtain the normalization facial image of 64 × 64 pixel sizes z0；
Step T2) from normalization facial image z0Middle extraction LBP feature vector L (z0)；
Step T3) from normalization facial image z0Middle extraction Gabor wavelet feature vector G (z0)；
Step T4) facial image z will be normalized0It zooms to 8 × 8 sizes, then by two dimensional image thaumatropy is one-dimensional Pixel characteristic vector P (z0)；
Step T5) by step T2), step T3), step T4) extract three kinds of textural characteristics be spliced into final feature to Measure D (z0)=(L (z0),G(z0),P(z0))；
Step T6) the feature vector D (z that obtains step T5)0)) be input in two-dimension human face fraud detection classifier, it obtains To testing result: facial image cheats image.
The present invention has the advantages that
1, the training method of two-dimension human face fraud detection classifier proposed by the present invention；By merge a variety of textural characteristics come Capture the subtle difference of face and corresponding photo；The complementary attribute of LBP, Gabor wavelet two powerful texture operators is absorbed, In, what LBP included is microtexture information, and what Gabor wavelet included is the information of macroscopic view, and pixel characteristic provides the overall situation Information, therefore the feature vector of training is fully extracted the information of the difference of face and photo；
2, in the training method of two-dimension human face fraud detection classifier proposed by the present invention, feature extraction is simple and efficient, It does not need user purposely to cooperate, good effect can be obtained in the case where low resolution；Two-dimension human face feature vector mentions It takes and combines SVR regression algorithm that lower error can be obtained when realizing face fraud detection, meet practical application scene needs；
3, the textural characteristics that the present invention uses can also be used to carry out recognition of face, be face fraud detection and recognition of face Provide a unique feature space；
4, face fraud detection method of the invention has the advantages that detection accuracy is high, face can be effectively prevent to cheat Behavior；
5, method of the invention can be applicable to many occasions such as face verification system, security monitoring.
Detailed description of the invention
Fig. 1 is the flow chart of the training method of two-dimension human face fraud detection classifier of the invention.
Specific embodiment
Technological concept of the present invention is briefly described below.
LBP (Local Binary Pattern, local binary model) is characterized in that face Related Research Domain is more common Textural characteristics.Most basic LBP image coding is defined as in 3 × 3 window, will be adjacent using window center pixel as threshold value The gray values of 8 pixels be compared with it, if surrounding pixel values are greater than center pixel value, the position of the pixel is marked It is denoted as 1, is otherwise 0.In this way, 8 points in 3 × 3 neighborhoods are compared and can produce 8 bits and (be typically converted into the decimal system Number is LBP codes, totally 256 kinds) to get to the LBP value of the window center pixel, and reflect with this value the texture in the region Information.In order to adapt to the textural characteristics of different scale, by 3 × 3 neighborhood extendings to any neighborhood, and with circle shaped neighborhood region instead of just Square Neighborhood calculates the gray value for not entirely falling within the point of location of pixels using bilinear interpolation algorithm.In addition, improved LBP operator allows to have any number of pixels in the round adjacent, domain that radius is R.LBP equivalent formulations (uniform Patterns) dimensionality reduction can be carried out to LBP feature vector.The circulation binary number corresponding to some local binary pattern from 0 to 1 or when be up to jumping twice from 1 to 0, binary system corresponding to the local binary pattern is known as an equivalent formulations Class, such as 00000000,11111111,10001111 be all equivalent formulations class, and the mode in addition to equivalent formulations class is all classified as separately One kind, referred to as mixed mode class.The type of improvement in this way, local binary patterns greatly reduces.Equivalent formulations class Zhan is total The overwhelming majority in mode can extract using the histogram of these equivalent formulations classes and mixed mode class more representative of image sheet The feature of matter characteristic.SymbolIndicate the equivalent formulations operator of the P point in the round field that radius is R.
Gabor wavelet is widely used in image recognition, field of image processing, in area of pattern recognition, Gabor wavelet Transformation is also a kind of very effective Feature Descriptor.Gabor wavelet is very sensitive for the edge of image, is capable of providing good Direction selection and scale selection characteristic, and it is insensitive for illumination variation, it is capable of providing to the good adaptability of illumination variation. Meanwhile the information of facial critical component (eyes, nose, mouth etc.) can be enhanced in two-dimensional Gabor function, so that retaining Enhance local characteristics while overall face information to be possibly realized.Gabor filter in airspace, one 2 dimension is a sine The product of plane wave and gaussian kernel function has and obtains the characteristic of optimal partial simultaneously in spatial domain and frequency domain, with the mankind Biological vision characteristic is much like, therefore can describe to correspond to spatial frequency (scale), spatial position and direction selection well The partial structurtes information of property.Substantially, Gabor wavelet transformation is the local message in order to extract signal Fourier transformation, is used One Gauss function is as window function, because of the Fourier transformation of a Gauss function or Gauss function, institute It is also local with Fourier inverse transformation.By the selection of frequency parameter and Gaussian function parameter, Gabor transformation can be chosen The characteristic information at many positions.Facial image is inputted after Gabor wavelet converts, one group of Gabor wavelet response diagram can be obtained As cluster.
The principle of principal component analysis (Principal Component Analysis, PCA) dimension reduction method is that removal is original Correlativity in data between each data component removes redundancy, retains most important ingredient.PCA calculates initial data association Feature vector corresponding to maximum several characteristic values and corresponding subspace is obtained in variance matrix, eigenvector projection is arrived The subspace is to achieve the purpose that sample space is described with small number of feature.Support vector machines is asked in solution classification It is that the criterion based on structural risk minimization finds an optimal separating hyper plane, by sample when topic (usually two classification problems) Originally be divided into two, reach different classes of have maximum class between be spaced.Discrete integer value is commonly used in classification problem indicates sample This classification, different from classification problem, the label of each sample in regression problem is continuous real number.Therefore support vector machines is returned Return (Support Vector Regression, SVR) to be intended to find a hyperplane, is capable of the distribution of Accurate Prediction sample, closely Like sample data.
Now in conjunction with the drawings and specific embodiments, the present invention will be further described.
As shown in Figure 1, a kind of training method of two-dimension human face fraud detection classifier, which comprises
Step S1) successively in training set i-th (1≤i≤L) a face picture is pre-processed, obtain 64 × 64 pixels The normalization facial image z of sizei(1≤i≤L)；It specifically includes:
Step S1-1) image gray processing processing is carried out to a face picture of i-th (1≤i≤L):
The face picture includes: true acquisition face picture and secondary imaging (forgery) face picture.
Face-image is traversed, each pixel is handled, the rgb value of each pixel is obtained, is distinguished by operation Red, blue, green value is extracted, optimum gradation conversion formula different according to sensitivity of the human eye to three kinds of colors of red blue green are as follows:
Grey=(9798R+19235G+3735B)/32768
Wherein Grey represent conversion after gray value, R, G, B respectively represent the red component of each pixel in image, Green component and blue component；
Step S1-2) gray level image size is adjusted to 64 × 64 using bilinear interpolation；
Step S1-3) image after being sized is enhanced:
The modification that image histogram is carried out using the statistical data of histogram, is gone out by adjusting each grey level pixel of image Existing probability is equal to change the pixel value of each grey level in image, to realize image enhancement；
Step S1-4) to enhanced image zooming-out image pixel matrix, obtain normalization facial image zi(1≤i≤ L)。
Step S2) from normalization facial image ziMiddle extraction LBP feature vector L (zi)；
The step S2) it specifically includes:
Step S2-1) to normalization facial image ziWithOperator obtains LBP image, then is by LBP image segmentation 3 × 3 overlapping regions, the statistic histogram for extracting 59 dimensions from each region respectively are finally synthesizing a 531 final dimensions Statistic histogram feature vector；
Wherein,Indicate the equivalent formulations operator of 8 points in the round field that radius is 1.
Step S2-2) to normalization facial image ziWithOperator, extraction obtain the statistic histogram feature of 59 dimensions Vector；
Wherein,Indicate the equivalent formulations operator of 8 points in the round field that radius is 2.
Step S2-3) to normalization facial image ziWithOperator extracts and obtains the statistic histogram spy of 243 dimensions Levy vector；
Wherein,Indicate the equivalent formulations operator of 16 points in the round field that radius is 2.
Step S2-4) feature vector that obtains step S2-1), step S2-2), step S2-3) synthesize a feature to Measure L (zi), feature vector L (zi) dimension be 59*9+59+243=833 dimension.
Step S3) the extraction Gabor wavelet feature vector G (z from normalization facial imagei)；
The step S3) it specifically includes:
Step S3-1) facial image z will be normalizedi32 × 32 sizes are zoomed to, it is small to carry out Gabor to the image after scaling Wave conversion:
The Gabor filter in p different directions and q different scale is taken to handle image after scaling, each picture Vegetarian refreshments t0It can obtain p × q Gabor amplitude Characteristics, p × q Gabor amplitude Characteristics cascade up a commonly known as Jet, It is abbreviated as J, then pixel t in image0Jet are as follows:
J(t0)=(M0,0(t0),...,M0,7(t0),...,M4,0(t0),...,M4,7(t0))
The Gabor amplitude Characteristics of all pixels point are cascaded up to obtain the feature vector F (z of facial imagei):
F(zi)={ J (t0):t0∈zi}
Step S3-2) determine step S3-1) obtained feature vector F (zi) dimensionality reduction dimension, be based on Principal Component Analysis To feature vector F (zi) carry out dimensionality reduction, the Gabor wavelet feature vector G (z after obtaining dimensionality reductioni)；It specifically includes:
Step S3-2-1) the feature vector F (z that obtains step S3-1)i) dimension d be divided into n equal part, determine new dimension Spend the value range of d'；
Original feature vector F (zi) dimension be d, the value of d is divided into n equal portions, value set is as follows:
Wherein,Indicate integer rounding operation；
If the feature that obtains after dimensionality reduction is G (zi), then its dimension d' takes this n value respectively:
Step S3-2-2) d' successively takes each value in set, calculate the corresponding face fraud inspection of all pictures of training set Survey mean absolute error set { MAEm}；
To the L picture in training set, calculate when d' takes each value in set, the corresponding people of all pictures of training set Face fraud detection mean absolute error is as follows:
Wherein, j indicates that jth picture in training set, k indicate that d' takes k-th of value in set, i.e.,lj Indicate the corresponding class label of jth picture in training set (0 indicates fraud image, and 1 indicates real human face image),Indicate training Concentrate the classification estimated value of jth picture；It may finally obtain different MAE value set { MAEmWherein m ∈ 1,2 ..., n；
Step S3-2-3) take set { MAEmIn minimum M AEmin, with MAEminCorresponding d' is tieed up as final dimensionality reduction Degree；
Step S3-2-4) it is based on step S3-2-3) obtained d', using Principal Component Analysis to feature vector F (zi) into Row dimensionality reduction, the Gabor wavelet feature vector G (z after obtaining dimensionality reductioni)。
Step S4) facial image z will be normalizediIt zooms to 8 × 8 sizes, then by two dimensional image thaumatropy is one-dimensional Pixel characteristic vector P (zi)；
Step S5) by step S2), step S3), step S4) extract three kinds of textural characteristics be spliced into final feature to Measure D (zi)=(L (zi),G(zi),P(zi))；
Step S6) based on Support vector regression algorithm to all feature vector D (zi), (1≤i≤L) is trained, Obtain two-dimension human face fraud detection classifier；Specifically comprise the following steps:
Step S6-1) based on Support vector regression algorithm building optimization problem；
Hypothesized model training set sample is { x(i),y(i)(i=1,2 ..., L), x(i)Indicate normalization facial image zi's Feature vector D (zi), y(i)Indicate the corresponding classification of the image: facial image or fraud image.Assuming that sample dimension is N, thenThe target of SVR is to solve for two-dimension human face fraud detection classifier f (x), makes f (x(i)) and y(i)Between difference It is a very small number no more than ε, ε, controls the worst error between practical label value and predictive estimation value.So, f (x) It is defined as follows:
Wherein, " " indicates inner product of vectors；W and b is the parameter solved；The w of solution should make | | w | |2It is minimum；Commonly referred to as should Hyperplane model is ε-SVR；So optimization problem of ε-SVR is represented by following formula:
SVR introduces penalty coefficient and slack variable to be adjusted:
Wherein,
Indicate loss function；
In the present embodiment, penalty coefficient C=128, the learning parameter g=0.1 in SVR parameter are set, using RBF (diameter To gaussian basis) kernel function is trained；
Step S6-2) optimization problem is converted using method of Lagrange multipliers, it is converted to and its antithesis is asked Topic is solved, and the expression formula of two-dimension human face fraud detection classifier f (x) is obtained；
Introduce following Lagrangian:
WhereinIndicate αiWithIndicate ηiWithAnd αi、ηiAndIt is Lagrange multiplier.It is public The solution of formula (9) belongs to convex quadratic programming problem scope, and by first solving L (w, α, η, b) function to w, the minimum value of b, ξ turn Change, then solve and meet L (w, α, η, b) function to w, the local derviation of b, ξ are respectively 0 " saddle point ":
It is availableTaking back in formula (9) can obtain:
In this way, the Optimal solution problem of solution formula (9) is converted into the solution of following dual problem:
In this way, just converting optimization problem only comprising an alpha parameter for problem, phase can be found out after obtaining the value of α The w answered, final f (x) are as follows:
According to KKT condition, when the dual problem of SVR only meets the following conditions, the solution of dual problem is just equivalent to original and asks The solution of topic:
It can be seen that and work as αiWhen=C, ξiJust it is not equal to 0, sample point can fall in ε (i.e. outlier) at this time, then:
The value of so b meets:
It is also seen that by KKT condition for | f (x)-y(i)|=ε+ξi (*)Sample point, it is correspondingJust it is not equal to 0； These sample point, that is, supporting vectors.
When kernel function is K (x(i), x) when, detection function f (x) becomes:
After training obtains SVR model, the only corresponding point of the supporting vector predicted value that decides recurrence.
Based on the two-dimension human face fraud detection classifier that above method training obtains, the present invention also provides two-dimension human faces to take advantage of Cheat detection method, which comprises
Step T1) collected to camera face picture to be detected pre-processes, obtain 64 × 64 pixel sizes Normalize facial image z0；
Step T2) from normalization facial image z0Middle extraction LBP feature vector L (z0)；
Step T3) from normalization facial image z0Middle extraction Gabor wavelet feature vector G (z0)；
Step T4) facial image z will be normalized0It zooms to 8 × 8 sizes, then by two dimensional image thaumatropy is one-dimensional Pixel characteristic vector P (z0)；
Step T5) by step T2), step T3), step T4) extract three kinds of textural characteristics be spliced into final feature to Measure D (z0)=(L (z0),G(z0),P(z0))；
Step T6) the feature vector D (z that obtains step T5)0)) be input to step S6) the fraud inspection of obtained two-dimension human face It surveys classifier f (x), obtains testing result: facial image or fraud image.
It should be noted last that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting.Although ginseng It is described the invention in detail according to embodiment, those skilled in the art should understand that, to technical side of the invention Case is modified or replaced equivalently, and without departure from the spirit and scope of technical solution of the present invention, should all be covered in the present invention Scope of the claims in.

Claims (3)

1. a kind of training method of two-dimension human face fraud detection classifier, which comprises first, to all in training set Face picture is pre-processed, and normalization facial image is obtained；Second, LBP feature is extracted from each normalization facial image Vector, Gabor wavelet feature vector sum one-dimensional pixel feature vector；These three feature vectors are spliced to form most by third Whole feature vector；4th, it is trained using support vector machines to final feature vector is spliced to form, obtains two-dimension human face Fraud detection classifier；
The method specifically includes:
Step S1) i-th of face picture in training set is pre-processed, wherein 1≤i≤L；Obtain 64 × 64 pixel sizes Normalization facial image zi；
Step S2) from normalization facial image ziMiddle extraction LBP feature vector L (zi)；
Step S3) from normalization facial image ziMiddle extraction Gabor wavelet feature vector G (zi)；
Step S4) facial image z will be normalizediIt zooms to 8 × 8 sizes, then by two dimensional image thaumatropy is one-dimensional pixel feature Vector P (zi)；
Step S5) by step S2), step S3), step S4) extract three kinds of textural characteristics be spliced into final feature vector D (zi)=(L (zi),G(zi),P(zi))；
Step S6) based on Support vector regression algorithm to all feature vector D (zi) be trained, it obtains two-dimension human face and takes advantage of Swindleness detection classifier, wherein 1≤i≤L；
The step S1) it specifically includes:
Step S1-1) image gray processing processing is carried out to face picture:
Face face picture is traversed, each pixel is handled, the rgb value of each pixel is obtained, is distinguished by operation Red, blue, green value is extracted, the gray value after calculating each pixel conversion:
Grey=(9798R+19235G+3735B)/32768
Wherein, Grey represents the gray value after conversion, and R, G, B respectively represent the red component of each pixel in image, green Colouring component and blue component；
Step S1-2) gray level image size is adjusted to 64 × 64 using bilinear interpolation；
Step S1-3) image after being sized is enhanced:
The modification of image histogram is carried out using the statistical data of histogram, is occurred by adjusting each grey level pixel of image general Rate is equal to change the pixel value of each grey level in image, to realize image enhancement；
Step S1-4) to enhanced image zooming-out image pixel matrix, obtain normalization facial image zi；
The step S2) it specifically includes:
Step S2-1) to normalization facial image ziWithOperator obtains LBP image, then by LBP image segmentation is 3 × 3 A overlapping region, the statistic histogram for extracting 59 dimensions from each region respectively synthesize the statistic histogram feature of one 531 dimension Vector；
Step S2-2) to normalization facial image ziWithOperator, extraction obtain the statistic histogram feature vector of 59 dimensions；
Step S2-3) to normalization facial image ziWithOperator, extract obtain the statistic histogram features of 243 dimensions to Amount；
Step S2-4) feature vector that obtains step S2-1), step S2-2), step S2-3) synthesizes a feature vector L (zi), the dimension of this feature vector is 59*9+59+243=833 dimension；
The step S3) it specifically includes:
Step S3-1) facial image z will be normalizedi32 × 32 sizes are zoomed to, Gabor wavelet change is carried out to the image after scaling It changes:
The Gabor filter in p different directions and q different scale is taken to handle image after scaling, each pixel t0 It can obtain p × q Gabor amplitude Characteristics, p × q Gabor amplitude Characteristics cascade up a referred to as Jet, are abbreviated as J, then Pixel t in image0Jet are as follows:
J(t0)=(M0,0(t0),...,M0,7(t0),...,M4,0(t0),...,M4,7(t0))
The Gabor amplitude Characteristics of all pixels point are cascaded up to obtain the feature vector F (z of facial imagei):
F(zi)={ J (t0):t0∈zi}
Step S3-2) determine step S3-1) obtained feature vector F (zi) dimensionality reduction dimension, based on Principal Component Analysis to feature Vector F (zi) carry out dimensionality reduction, the Gabor wavelet feature vector G (z after obtaining dimensionality reductioni)；
The step S3-2) it specifically includes:
Step S3-2-1) the feature vector F (z that obtains step S3-1)i) dimension d be divided into n equal part, determine new dimension d''s Value range；
Original feature vector F (zi) dimension be d, the value of d is divided into n equal portions, value set is as follows:
Wherein,Indicate integer rounding operation；
If the feature that obtains after dimensionality reduction is G (zi), then its dimension d' takes this n value respectively:
Step S3-2-2) d' successively takes each value in set, it is flat to calculate the corresponding face fraud detection of all pictures of training set Equal absolute error set { MAEk}；
To the L picture in training set, calculate when d' takes each value in set, the corresponding face of all pictures of training set is taken advantage of Swindleness detection mean absolute error is as follows:
Wherein, j indicates that jth picture in training set, k indicate that d' takes k-th of value in set, i.e.,ljIt indicates The corresponding class label of jth picture in training set: 0 indicates fraud image, and 1 indicates real human face image,It indicates in training set The classification estimated value of jth picture；It may finally obtain different MAE value set { MAEkWherein k ∈ 1,2 ..., n；
Step S3-2-3) take set { MAEkIn minimum M AEmin, with MAEminCorresponding d' is as final dimensionality reduction dimension；
Step S3-2-4) it is based on step S3-2-3) obtained d', using Principal Component Analysis to feature vector F (zi) dropped Dimension, the Gabor wavelet feature vector G (z after obtaining dimensionality reductioni)。
2. the training method of two-dimension human face fraud detection classifier according to claim 1, which is characterized in that the step S6 it) specifically includes:
Step S6-1) based on Support vector regression algorithm building optimization problem；
Hypothesized model training set sample is { x(i),y(i), i=1,2 ..., L, x(i)Indicate normalization facial image ziFeature to Measure D (zi), y(i)Indicate the corresponding classification of the image: facial image or fraud image；Assuming that sample dimension is N, then The target of Support vector regression algorithm is to solve for two-dimension human face fraud detection classifier f (x), makes f (x(i)) and y(i)Between Difference is not more than ε, and ε is threshold value, controls the worst error between practical label value and predictive estimation value；So, f (x) determines Justice is as follows:
Wherein, " " indicates inner product of vectors；W and b is the parameter solved；
Step S6-2) optimization problem (1) is converted using method of Lagrange multipliers, it is converted to its dual problem It is solved, obtains the expression formula of two-dimension human face fraud detection classifier f (x).
3. a kind of face fraud detection method, the two-dimension human face obtained based on the training of method described in one of claim 1-2 is taken advantage of Swindleness detection classifier realization, this method comprises:
Step T1) face picture to be detected is pre-processed, obtain the normalization facial image z of 64 × 64 pixel sizes0；
Step T2) from normalization facial image z0Middle extraction LBP feature vector L (z0)；
Step T3) from normalization facial image z0Middle extraction Gabor wavelet feature vector G (z0)；
Step T4) facial image z will be normalized08 × 8 sizes are zoomed to, then two dimensional image thaumatropy is special for one-dimensional pixel Levy vector P (z0)；
Step T5) by step T2), step T3), step T4) extract three kinds of textural characteristics be spliced into final feature vector D (z0)=(L (z0),G(z0),P(z0))；
Step T6) the feature vector D (z that obtains step T5)0)) be input in two-dimension human face fraud detection classifier, it is examined Survey result: facial image or fraud image.
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