CN102663370A - Face identification method and system - Google Patents

Face identification method and system Download PDF

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CN102663370A
CN102663370A CN2012101202656A CN201210120265A CN102663370A CN 102663370 A CN102663370 A CN 102663370A CN 2012101202656 A CN2012101202656 A CN 2012101202656A CN 201210120265 A CN201210120265 A CN 201210120265A CN 102663370 A CN102663370 A CN 102663370A
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similarity
sample
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based learning
dimensionality reduction
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CN102663370B (en
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张莉
夏佩佩
冷亦琴
何书萍
王邦军
李凡长
杨季文
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Suzhou University
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Abstract

The invention discloses a face identification method, which comprises the following steps of: obtaining a classifier model by carrying out random dimension reduction on testing samples and training samples, generating a training set and a testing set of the similarity learning, selecting a regular parameter and a Gaussian kernel function of a support vector machine and inputting the training set of the similarity learning into the regular parameter and the Gaussian kernel function; then inputting the testing set of the similarity learning into the classifier model to obtain classification results; obtaining a sum of the classification results, using the quotient of the sum and the sample number of a certain type of samples as a value of the similarity probability of the type, obtaining the maximum value and outputting the maximum value to obtain the value of the similarity probability so as to obtain the most accurate face identification result. By dimension reduction of the samples, the complexity of the samples is reduced, so that an algorithm of learning the similarity between face images on the basis of the SVM (Support Vector Machine) is rapid; and moreover, by an algorithm of carrying out processing on each type, the face identification rate is correspondingly improved.

Description

A kind of method and system of recognition of face
Technical field
The present invention relates to technical field of biometric identification, in particular, relate to a kind of method and system of recognition of face.
Background technology
In decades in the past, recognition of face has developed into a popular research topic in the computer vision, also is in a most successful application of art of image analysis simultaneously.Nowadays, the research of recognition of face has major and immediate significance, uses with input in case study successfully, will produce huge social and economic benefit.In the research algorithm of recognition of face, mainly be divided into two types.One type of face recognition algorithms that is based on image, the another kind of face recognition algorithms that is based on image.Face recognition algorithms based on image starts to walk early, and technology is comparatively ripe; Face recognition algorithms based on image is difficult with respect to the face recognition algorithms based on image, is just to begin development in recent years, has also obtained certain achievement at present.
P.J.Phillips has proposed to utilize SVM (Support Vector Machines, SVMs) to learn the similarity between the facial image, thereby carries out recognition of face.In similarity-based learning, it is right that the building method that Phillips has proposed difference space is constructed sample, in difference space, studies same type of difference and the difference between the inhomogeneity individuality between the individual different images emphatically.Experimental result shows that this method is compared with traditional method based on PCA, has certain advantage really.But the sample complexity of difference space method is very high, and n width of cloth facial image is for example arranged, and then in difference space, can produce n 2Individual training sample is right, adopts SVM to train again, and what computation complexity also can be very is big, even situation about can not optimize can occur; In addition, because the difference space sample may lost part information to building method,, increase the difficulty of similarity-based learning so, also can make the sample in the difference space serious overlapping to occurring even former two types of samples have good separability.
Therefore, providing a kind of fast based on the method and system of the recognition of face of SVM similarity-based learning, improve the efficient and the difficulty that reduces similarity-based learning of recognition of face, is those skilled in the art's problem demanding prompt solutions.
Summary of the invention
In view of this, the invention provides a kind of method and system of recognition of face, because the sample complexity of difference space method is very high, cause the problem of the difficulty increase of computation complexity and similarity-based learning to overcome in the prior art.
For realizing above-mentioned purpose, the present invention provides following technical scheme:
A kind of method of recognition of face is characterized in that, learns the similarity between the facial image based on SVMs SVM, comprising:
To the original training sample of people's face training data structure similarity-based learning, and said original training sample carried out dimensionality reduction at random, generate the similarity-based learning training set;
Adopt SVMs to train the training set of said similarity-based learning, generate sorter model;
To the original test sample book of people's face test data structure similarity-based learning, and said original test sample book carried out dimensionality reduction at random, generate the similarity-based learning test set;
According to said sorter model said similarity-based learning test set is classified; Obtain the classification results of said similarity-based learning test set; Classification results according to said similarity-based learning test set is added up the similarity probability between each type training sample in sample to be tested and the said original training sample according to preset rules; And, obtain face recognition result with said similarity probability maximal value output.
Wherein, said said original training sample is carried out dimensionality reduction at random, generates the similarity-based learning training set and be specially:
After carrying out at random dimensionality reduction, generate the training sample set behind the dimensionality reduction, keep the classification mark behind the dimensionality reduction constant;
For any two samples that the test sample book behind the said dimensionality reduction is concentrated, it is right to generate two different binary samples, judges whether two sample class are identical; If; Then said new binary is+1 to the classification value of the training sample set of form, and if promptly positive sample is not; Then said new binary is-1 to the classification value of the training sample set of form, i.e. negative sample;
Each sample is found out K nearest foreign peoples's point with the method for k nearest neighbor, said foreign peoples's point is stored in set X i kIn, generate the similarity-based learning training set.
Wherein, said training set with said similarity-based learning adopts SVMs to train, and generates sorter model and is specially:
Select the regular parameter and the gaussian kernel function of said SVMs, the training set of said similarity-based learning is imported said regular parameter and gaussian kernel function, then obtain sorter model.
Wherein, said said original test sample book is carried out dimensionality reduction at random, generates the similarity-based learning test set and be specially:
After carrying out at random dimensionality reduction, generate the sample collection to be tested behind the dimensionality reduction, keep the classification mark behind the dimensionality reduction constant;
Sample and each sample generation binary sample in the training set behind the said dimensionality reduction for the sample to be tested behind the said dimensionality reduction is concentrated are right, and said binary sample is to being said similarity-based learning test set.
Wherein, said classification results according to said similarity-based learning test set adds up according to preset rules that the similarity probability between each type training sample is specially in sample to be tested and the said original training sample:
Each said classification results sum with said similarity-based learning test set; Measure the merchant with the sample number of the original test sample book of each type respectively, with the said result that obtains of merchant that gets as the similarity probability between each type training sample in sample to be tested and the said original training sample.
The similarity between the facial image is learnt based on SVMs SVM by a kind of system of recognition of face, comprising:
The training pre-processing module is used for the original training sample to people's face training data structure similarity-based learning, and said original training sample is carried out dimensionality reduction at random, generates the similarity-based learning training set;
The training pattern module is used for adopting SVMs to train the training set of said similarity-based learning, generates sorter model;
The test pre-processing module is used for the original test sample book to people's face test data structure similarity-based learning, and said original test sample book is carried out dimensionality reduction at random, generates the similarity-based learning test set;
Test module; Be used for the test set of said similarity-based learning being classified according to said sorter model; Obtain the classification results of said similarity-based learning test set; Classification results according to said similarity-based learning test set is added up the similarity probability between each type training sample in sample to be tested and the said original training sample according to preset rules, and with said similarity probability maximal value output, obtains face recognition result.
Can know via above-mentioned technical scheme, compared with prior art, the invention discloses a kind of method of recognition of face; Through test sample book and training sample are carried out dimensionality reduction at random, and generate similarity-based learning training set and test set, select the regular parameter and the gaussian kernel function of SVMs; The training set of similarity-based learning is input in regular parameter and the gaussian kernel function, obtains sorter model, the test set with similarity-based learning is input in the sorter model again; Obtain classification results, through said classification results is sued for peace, with the merchant of the sample size of a certain type of sample be the size of the similarity probability of said a certain class; Obtain maximal value; And, obtain the size of similarity probability with said maximal value output, obtain face recognition result the most accurately.Through dimensionality reduction to sample, the sample complexity is reduced, make that to learn the algorithm of the similarity between the facial image based on SVM quick; In addition, through the algorithm that carries out for each type, make the recognition of face rate that corresponding raising arranged.
Description of drawings
In order to be illustrated more clearly in the embodiment of the invention or technical scheme of the prior art; To do to introduce simply to the accompanying drawing of required use in embodiment or the description of the Prior Art below; Obviously, the accompanying drawing in describing below only is embodiments of the invention, for those of ordinary skills; Under the prerequisite of not paying creative work, can also obtain other accompanying drawing according to the accompanying drawing that provides.
Fig. 1 is the method flow diagram of the disclosed a kind of recognition of face of the embodiment of the invention;
Fig. 2 is the structural drawing of the system of the disclosed a kind of recognition of face of the embodiment of the invention;
Fig. 3 is the prediction synoptic diagram of the disclosed a kind of recognition of face of the embodiment of the invention.
Embodiment
To combine the accompanying drawing in the embodiment of the invention below, the technical scheme in the embodiment of the invention is carried out clear, intactly description, obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills are not making the every other embodiment that is obtained under the creative work prerequisite, all belong to the scope of the present invention's protection.
The invention discloses a kind of method of recognition of face, through test sample book and training sample are carried out dimensionality reduction at random, and generate similarity-based learning training set and test set; Select the regular parameter and the gaussian kernel function of SVMs, the training set of similarity-based learning is input in regular parameter and the gaussian kernel function, obtain sorter model; Test set with similarity-based learning is input in the sorter model again; Obtain classification results, through said classification results is sued for peace, with the merchant of the sample size of a certain type of sample be the size of the similarity probability of said a certain class; Obtain maximal value; And, obtain the size of similarity probability with said maximal value output, obtain face recognition result the most accurately.Through dimensionality reduction to sample, the sample complexity is reduced, make that to learn the algorithm of the similarity between the facial image based on SVM quick; In addition, through the algorithm that carries out for each type, make the recognition of face rate that corresponding raising arranged.
The test that the present invention carries out in the ORL face database, the ORL face database is created by Cambridge University AT&T laboratory, comprises 40 people, everyone 10 secondary pictures, totally 400 face-images.Aspects such as the illumination of shot picture, countenance are not quite similar.This face database has been taken into account aspects such as ethnic group, sex and facial expression, is a kind of commonly used, database of carrying out recognition of face.5 of every type of picked at random are as training set from 40 types of samples, in every type other 5 as test set, each 200 sample in training set, the test set.
See also accompanying drawing 1, be the method flow diagram of the disclosed a kind of recognition of face of the embodiment of the invention.The method of the disclosed a kind of recognition of face of the embodiment of the invention, the practical implementation step is:
Step 101: to the original training sample of people's face training data structure similarity-based learning, and said original training sample carried out dimensionality reduction at random, generate the similarity-based learning training set.
This step is mainly handled people's face training data, the training sample of structure similarity-based learning.If existing people's face training dataset does
Figure BDA0000156217040000051
X wherein i∈ R DBe someone's face data, y i=1,2 ..., c} representes x iClassification.In the present embodiment, 1=200, D=10304, c=40.
The sample of at first people's face training data being concentrated carries out dimensionality reduction at random, and sample is all dropped to the d dimension, and (d＜D) keeps the classification mark behind the dimensionality reduction constant simultaneously.Then the training sample set behind the dimensionality reduction is the training sample behind the dimensionality reduction for
Figure BDA0000156217040000052
Figure BDA0000156217040000053
; In the present embodiment experiment, d=100.
At random behind the dimensionality reduction; Generate the training set of new binary to form; For any two samples
Figure BDA0000156217040000054
and
Figure BDA0000156217040000055
; It is identical with the classification of
Figure BDA0000156217040000059
as if
Figure BDA0000156217040000058
with
Figure BDA0000156217040000057
to
Figure BDA0000156217040000056
to produce two different binary samples; The classification value of then remembering new sample is+1, promptly positive sample; Otherwise note-1, i.e. negative sample.In order to reduce the right number of sample; Improve the efficient of algorithm; Each sample
Figure BDA00001562170400000510
is found out its K nearest foreign peoples's point with the method for k nearest neighbor, be stored in these these foreign peoples's points in the set
Figure BDA00001562170400000511
.Then the training set of similarity-based learning can be expressed as
Figure BDA00001562170400000512
Z wherein q∈ R 2d, m is the right number of sample, and if y i=y j, then y ‾ q = + 1 , z q = ( x ‾ i , x ‾ j ) ; If y i≠ y j, then y ‾ q = - 1 , z q = ( x ‾ i , x ‾ j ) , x ‾ j ∈ X i K . In this experiment, K=9, m=5200.
Step 102: adopt SVMs to train the training set of said similarity-based learning, generate sorter model.
This step mainly is the training set to the similarity-based learning of last step generation, adopts SVMs to train.Select the regular parameter of SVMs and parameter σ＞0 of gaussian kernel function
Figure BDA0000156217040000061
; The set of input training sample, then the sorter model of gained is:
f ( z ) = sgn ( Σ i = 1 m α i y ‾ i k ( z i , z ) + b )
Wherein: α iWith b is to train tight model coefficient of giving birth to by SVMs,
Figure BDA0000156217040000063
The expression sign function.
Step 103: to the original test sample book of people's face test data structure similarity-based learning, and said original test sample book carried out dimensionality reduction at random, generate the similarity-based learning test set.
This step is mainly handled people's face test data, the test sample book of structure similarity-based learning.
If any sample x ∈ R to be tested D, people's face test data sample is carried out dimensionality reduction at random, method is with identical to the processing of training set, and the dimension of people's face test data is dropped to the d dimension, and the sample to be tested behind the dimensionality reduction does
Figure BDA0000156217040000064
At random behind the dimensionality reduction; Produce the test set of new binary sample to form; With each sample
Figure BDA0000156217040000066
in the training set behind test sample book and the dimensionality reduction treated behind the dimensionality reduction all generate the binary sample to
Figure BDA0000156217040000067
then the test set of similarity-based learning be expressed as
Figure BDA0000156217040000068
Figure BDA0000156217040000069
q=1; N, n are the right number of sample.
In the present embodiment experiment, sample x ∈ R to be tested 10304, people's face test data sample is carried out dimensionality reduction at random, with the dimension dimensionality reduction to 100 of people's face test data, the sample to be tested behind the dimensionality reduction does Behind the dimensionality reduction, produce the test set of new binary sample to form at random, this test set can be expressed as z q Test = ( x ‾ , x ‾ i ) , z q Test ∈ R 200 , Q=1 ..., 200.
Step 104: said similarity-based learning test set is classified according to said sorter model; Obtain the classification results of said similarity-based learning test set; Classification results according to said similarity-based learning test set is added up the similarity probability between each type training sample in sample to be tested and the said original training sample according to preset rules; And, obtain face recognition result with said similarity probability maximal value output.
This step is right according to the sample that the processing in the step 103 obtains with sample to be tested x
Figure BDA00001562170400000612
Be input in the sorter model of step 102 generation, replace the z in the former sorter model, can obtain n classification results, can be expressed as f (z successively i), i=1 ..., n.According to classification results, can count the similarity probability size of each type in test sample book x and the former training sample, and x classified according to following rule:
Wherein: l pThe expression classification is the sample size of p, and
Figure BDA0000156217040000072
In this experiment, it is right that sample x to be tested obtains sample according to the processing in the step 103
Figure BDA0000156217040000073
Be input in the sorter model of step 102 generation, replace the z in the former sorter model, can obtain 200 classification results, can be expressed as f (z successively i), i=1 ..., 200.According to following rule x is classified:
According to classification results, add up the similarity probability of each type in original test sample book and the original training sample, and similarity probability maximal value is exported according to rule, obtain face recognition result the most accurately.
The method of a kind of recognition of face disclosed by the invention through test sample book and training sample are carried out dimensionality reduction at random, and generates the training set and the test set of similarity-based learning; Select the regular parameter and the gaussian kernel function of SVMs, the training set of similarity-based learning is input in regular parameter and the gaussian kernel function, obtain sorter model; Test set with similarity-based learning is input in the sorter model again; Obtain classification results, through said classification results is sued for peace, with the merchant of the sample size of a certain type of sample be the size of the similarity probability of said a certain class; Obtain maximal value; And, obtain the size of similarity probability with said maximal value output, obtain face recognition result the most accurately.Through dimensionality reduction to sample, the sample complexity is reduced, make that to learn the algorithm of the similarity between the facial image based on SVM quick; In addition, through the algorithm that carries out for each type, make the recognition of face rate that corresponding raising arranged.
Describe method in detail among the disclosed embodiment of the invention described above, can adopt the system of various ways to realize, therefore the invention also discloses a kind of system, provide concrete embodiment below and be elaborated for method of the present invention.
See also accompanying drawing 2, be the system construction drawing of the disclosed a kind of recognition of face of the embodiment of the invention.The invention discloses a kind of system of recognition of face, this system specifically comprises:
Training pre-processing module 201 is used for the original training sample to people's face training data structure similarity-based learning, and said original training sample is carried out dimensionality reduction at random, generates the similarity-based learning training set.Training pattern module 202 is used for adopting SVMs to train the training set of said similarity-based learning, generates sorter model.Test pre-processing module 203 is used for the original test sample book to people's face test data structure similarity-based learning, and said original test sample book is carried out dimensionality reduction at random, generates the similarity-based learning test set.Test module 204; Be used for the test set of said similarity-based learning being classified according to said sorter model; Obtain the classification results of said similarity-based learning test set; Classification results according to said similarity-based learning test set is added up the similarity probability between each type training sample in sample to be tested and the said original training sample according to preset rules, and with said similarity probability maximal value output, obtains face recognition result.
Training pre-processing module 201 is mainly handled people's face training data, the training sample of structure similarity-based learning.If existing people's face training dataset does
Figure BDA0000156217040000081
X wherein i∈ R DBe someone's face data, y i=1,2 ..., c} representes x iClassification.
The sample of at first people's face training data being concentrated carries out dimensionality reduction at random, and sample is all dropped to the d dimension, and (d＜D) keeps the classification mark behind the dimensionality reduction constant simultaneously.The dimensionality reduction of training sample set is
Figure BDA0000156217040000082
Figure BDA0000156217040000083
is the dimensionality reduction of training samples.
After the dimensionality reduction, generate the training set of new binary now at random to form.For any two samples
Figure BDA0000156217040000084
and
Figure BDA0000156217040000085
; It is identical with the classification of
Figure BDA0000156217040000089
as if
Figure BDA0000156217040000088
with
Figure BDA0000156217040000087
to
Figure BDA0000156217040000086
to produce two different binary samples; The classification value of then remembering new sample is+1, promptly positive sample; Otherwise note-1, i.e. negative sample.In order to reduce the right number of sample; Improve the efficient of algorithm; Each sample is found out its K nearest foreign peoples's point with the method for k nearest neighbor, be stored in these these foreign peoples's points in the set
Figure BDA00001562170400000811
.Then the training set of similarity-based learning can be expressed as
Figure BDA00001562170400000812
Z wherein q∈ R 2d, m is the right number of sample, and if y i=y j, then y ‾ q = + 1 , z q = ( x ‾ i , x ‾ j ) ; If y i≠ y j, then y ‾ q = - 1 , z q = ( x ‾ i , x ‾ j ) , x ‾ j ∈ X i K .
Training pattern module 202 mainly is that the training set employing SVMs of the similarity-based learning of training pre-processing module generation is trained.Select the regular parameter of SVMs and parameter σ＞0 of gaussian kernel function
Figure BDA0000156217040000091
; The set of input training sample, then the sorter model of gained is:
f ( z ) = sgn ( Σ i = 1 m α i y ‾ i k ( z i , z ) + b )
Wherein: α iWith b is to train the model coefficient that produces by SVMs,
Figure BDA0000156217040000093
The expression sign function.
Test pre-processing module 203 is mainly handled people's face test data, the test sample book of structure similarity-based learning.If any sample x ∈ R to be tested D, people's face test data sample is carried out dimensionality reduction at random, method is identical to the processing of training set in the pre-processing module with training, and the dimension of people's face test data is dropped to the d dimension, and the sample to be tested behind the dimensionality reduction does
Figure BDA0000156217040000094
After the dimensionality reduction, produce the test set of new binary sample at random to form.With each sample
Figure BDA0000156217040000096
in the training set behind test sample book
Figure BDA0000156217040000095
and the dimensionality reduction treated behind the dimensionality reduction all generate the binary sample to
Figure BDA0000156217040000097
then the test set of similarity-based learning be expressed as
Figure BDA0000156217040000098
q=1; N, n are the right number of sample.
Test module 204 is right according to the sample that the processing in the test pre-processing module 203 obtains with sample to be tested x
Figure BDA00001562170400000910
Be input in the sorter model of training module generation, replace the z in the former sorter model, can obtain n classification results, can be expressed as f (z successively i), i=1 ..., n.According to classification results, can count the similarity probability size of each type in test sample book x and the former training sample, and x classified according to following rule:
Figure BDA00001562170400000911
Wherein: l pThe expression classification is the sample size of p, and
Figure BDA00001562170400000912
On training sample, adopt 5 times of cross validations to select parameter, wherein the span of regular factor is { 2 -4, 2 -3..., 2 9, Gauss's radial kernel function parameters span is { 2 -9, 2 -8..., 2 4.Come to train again a model with the parameter of select then, obtain discrimination at the test set estimated performance.Selecting of training sample and test sample book is at random, and we repeat 10 tests.See also table 1, be the error rate contrast table of the present invention and difference space method, provided the error rate and the average result of 10 experiments.Comparative approach has two kinds of forms of difference space: difference sample and difference absolute value sample, and method of the present invention.The method of difference space has also been used the k nearest neighbor method, otherwise can't carry out emulation.
Figure BDA0000156217040000101
The error rate contrast of table 1. the present invention and difference space method
See also accompanying drawing 3, be the prediction synoptic diagram of the disclosed a kind of recognition of face of the embodiment of the invention.
We can find out that recognition of face effect of the present invention obviously is superior to difference sample and difference absolute value sample through experimental result, and have shown stronger stability, have certain advantage.
In sum: the invention discloses a kind of method of recognition of face, through test sample book and training sample are carried out dimensionality reduction at random, and generate the training set and the test set of similarity-based learning; Select the regular parameter and the gaussian kernel function of SVMs, the training set of similarity-based learning is input in regular parameter and the gaussian kernel function, obtain sorter model; Test set with similarity-based learning is input in the sorter model again; Obtain classification results, through said classification results is sued for peace, with the merchant of the sample size of a certain type of sample be the size of the similarity probability of said a certain class; Obtain maximal value; And, obtain the size of similarity probability with said maximal value output, obtain face recognition result the most accurately.Through dimensionality reduction to sample, the sample complexity is reduced, make that to learn the algorithm of the similarity between the facial image based on SVM quick; In addition, through the algorithm that carries out for each type, make the recognition of face rate that corresponding raising arranged.
For the disclosed system of embodiment, because it is corresponding with the embodiment disclosed method, so description is fairly simple, relevant part is partly explained referring to method and is got final product.
To the above-mentioned explanation of the disclosed embodiments, make this area professional and technical personnel can realize or use the present invention.Multiple modification to these embodiment will be conspicuous concerning those skilled in the art, and defined General Principle can realize under the situation that does not break away from the spirit or scope of the present invention in other embodiments among this paper.Therefore, the present invention will can not be restricted to these embodiment shown in this paper, but will meet and principle disclosed herein and features of novelty the wideest corresponding to scope.

Claims (6)

1. the method for a recognition of face is characterized in that, learns the similarity between the facial image based on SVMs SVM, comprising:
To the original training sample of people's face training data structure similarity-based learning, and said original training sample carried out dimensionality reduction at random, generate the similarity-based learning training set;
Adopt SVMs to train the training set of said similarity-based learning, generate sorter model;
To the original test sample book of people's face test data structure similarity-based learning, and said original test sample book carried out dimensionality reduction at random, generate the similarity-based learning test set;
According to said sorter model said similarity-based learning test set is classified; Obtain the classification results of said similarity-based learning test set; Classification results according to said similarity-based learning test set is added up the similarity probability between each type training sample in sample to be tested and the said original training sample according to preset rules; And, obtain face recognition result with said similarity probability maximal value output.
2. method according to claim 1 is characterized in that, said said original training sample is carried out dimensionality reduction at random, generates the similarity-based learning training set and is specially:
After carrying out at random dimensionality reduction, generate the training sample set behind the dimensionality reduction, keep the classification mark behind the dimensionality reduction constant;
For any two samples that the test sample book behind the said dimensionality reduction is concentrated, it is right to generate two different binary samples, judges whether two sample class are identical; If; Then said new binary is+1 to the classification value of the training sample set of form, and if promptly positive sample is not; Then said new binary is-1 to the classification value of the training sample set of form, i.e. negative sample;
Each sample is found out K nearest foreign peoples's point with the method for k nearest neighbor, said foreign peoples's point is stored in set X i kIn, generate the similarity-based learning training set.
3. method according to claim 1 is characterized in that, said training set with said similarity-based learning adopts SVMs to train, and generates sorter model and is specially:
Select the regular parameter and the gaussian kernel function of said SVMs, the training set of said similarity-based learning is imported said regular parameter and gaussian kernel function, then obtain sorter model.
4. method according to claim 1 is characterized in that, said said original test sample book is carried out dimensionality reduction at random, generates the similarity-based learning test set and is specially:
After carrying out at random dimensionality reduction, generate the sample collection to be tested behind the dimensionality reduction, keep the classification mark behind the dimensionality reduction constant;
Sample and each sample generation binary sample in the training set behind the said dimensionality reduction for the sample to be tested behind the said dimensionality reduction is concentrated are right, and said binary sample is to being said similarity-based learning test set.
5. method according to claim 1 is characterized in that, said classification results according to said similarity-based learning test set adds up according to preset rules that the similarity probability between each type training sample is specially in sample to be tested and the said original training sample:
Each said classification results sum with said similarity-based learning test set; Measure the merchant with the sample number of the original test sample book of each type respectively, with the said result that obtains of merchant that gets as the similarity probability between each type training sample in sample to be tested and the said original training sample.
6. the system of a recognition of face is characterized in that, learns the similarity between the facial image based on SVMs SVM, comprising:
The training pre-processing module is used for the original training sample to people's face training data structure similarity-based learning, and said original training sample is carried out dimensionality reduction at random, generates the similarity-based learning training set;
The training pattern module is used for adopting SVMs to train the training set of said similarity-based learning, generates sorter model;
The test pre-processing module is used for the original test sample book to people's face test data structure similarity-based learning, and said original test sample book is carried out dimensionality reduction at random, generates the similarity-based learning test set;
Test module; Be used for the test set of said similarity-based learning being classified according to said sorter model; Obtain the classification results of said similarity-based learning test set; Classification results according to said similarity-based learning test set is added up the similarity probability between each type training sample in sample to be tested and the said original training sample according to preset rules, and with said similarity probability maximal value output, obtains face recognition result.
CN 201210120265 2012-04-23 2012-04-23 Face identification method and system Expired - Fee Related CN102663370B (en)

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