CN102622616A - Human face recognition method based on two-dimensional kernel principal component analysis and fuzzy maximum scatter difference - Google Patents
Human face recognition method based on two-dimensional kernel principal component analysis and fuzzy maximum scatter difference Download PDFInfo
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Abstract
A human face recognition method based on two-dimensional kernel principal component analysis and fuzzy maximum scatter difference includes steps of firstly, effectively extracting a non-linear structural feature of a human face by a K2DPCA (two-dimensional kernel principal component analysis) method; secondly, selecting a feature vector with between-class scatter larger than in-class scatter after projection as an optimal projection axis, and accordingly leading feature vectors corresponding to small feature values to be choices of the optimal projection axis so as to fuse subtle expression change information of the human face; and thirdly, redefining a scatter matrix of samples according to a membership degree function by the aid of advantages of FMSD (fuzzy maximum scatter difference), and sufficiently integrating original distribution information of the samples into feature extraction of the human face via corresponding membership degree information. Effective improvement is made according to hard classification problems in human face recognition, and the problem that a judgment and analysis method based on bidirectional maximum scatter difference cannot effectively extract non-linear discriminant features of the human face in terms of human face recognition, and has edge classes and hard classification.
Description
Technical field
The present invention relates to a kind of face identification method; Relate in particular to a kind of face identification method based on two-dimensional nucleus principal component analysis (PCA) and fuzzy maximum divergence difference and belong to pattern-recognition and computer vision research category, the major technique field relates to extracts effective people's face diagnostic characteristics and sorter reasonable in design.
Background technology
Recognition of face has become important research direction in the living things feature recognition, mainly carries out through the sorter that extracts effective people's face diagnostic characteristics and complex design.Wherein principal component analysis (PCA) (Principle Components Analysis is called for short PCA) method and linear discriminant analysis (Linear Discriminant Analysis is called for short LDA) method all are effective linear characteristic extracting methods.
The PCA method is redeveloped into purpose with optimum, through maximizing the optimum projector space that the total volume divergence of training sample obtains sample, is not suitable for classification problem.LDA is a purpose with the diagnostic characteristics that extracts the Different Individual facial image; A between class scatter and type ratio of interior divergence through the maximization training sample; Can extract the authentication information between all kinds of effectively; But in computation process, need to guarantee that scatter matrix is reversible in the class, and recognition of face is typical higher-dimension, small sample problem that the divergence matrix is unusual often in type.In order to address this problem; People such as Belhumeur have proposed Fisherface method (Fisherlinear Discriminant Analysis; Be called for short FLDA) [Belhumeur P N; Hespanha Joao P; Kriegman David J. Eigenfaces vs. Fisherfaces:Recognition using class specific linear projection [J]. IEEE Trans. Pattern Analysis and Machine Intelligence, 1997,19 (7): 711-720.].This method at first utilizes PCA that sample is carried out dimensionality reduction, makes the interior divergence matrix of class of sample nonsingular, utilizes LDA to obtain to differentiate projector space then.Yet selectivity PCA dimensionality reduction can not guarantee the nonsingularity of between class scatter matrix.Can't directly find the solution the problem of optimum axis of projection in order fundamentally to eliminate traditional F isher discriminating criterion because of the divergence matrix is unusual in the class that small sample problem caused; People such as Song Fengxi have proposed the face identification method [Song Fengxi of maximum divergence poor (MSD) and big spacing linear projection and SVMs; Cheng Ke, Yang Jingyu, etc. maximum divergence difference and big spacing linear projection and SVMs [J]. the robotization journal; 2004,30 (6): 890-896.].MSD utilizes the difference of between class scatter and type interior divergence as the sorter criterion, need not construct inverse matrix, has not only solved the small sample problem in the recognition of face effectively, has also improved the speed of algorithm.But MSD is a kind of two types of sorters in essence, and in order to solve the multiclass pattern recognition problem, Song Fengxi etc. have proposed the people's face method for expressing based on the maximum divergence difference of multiclass, extracts effective people's face diagnostic characteristics through setting up the maximum divergence difference discriminating of multiclass criterion.In order to obtain
value suitable among the MSD; Song Fengxi etc. have proposed to differentiate based on maximum divergence difference adaptive classification algorithm (the AMSD) [Song Fengxi of criterion; Magnify roc; Yang Jingyu; Gao Xiumei. differentiate the adaptive classification algorithm [J] of criterion based on maximum divergence difference. robotization journal, 2006,32 (4): 541-549.].Yet there is the deficiency of two aspects in AMSD: the one, parameter
value to choose scope bigger; The 2nd, there is the suboptimality problem in the MSD method.Value as
hour; The relative between class scatter of divergence is enough little in can not type of assurance, makes between foreign peoples's sample and enough separates; When the value as
is enough big; Divergence goes to zero in type; Be equivalent to the class average of the distribution or accumulation of all kinds of samples, so the recognition of face performance depends on the order of accuarcy that sample average is calculated in such sample.But in face recognition process, people's face number of training is generally less on the one hand, can not accurately calculate sample average; The opposing party's dough figurine face sample puts a certain type hard classification problem because of influenced by extraneous factors such as illumination, expression to produce under away from the caused edge of the sample class problem at actual type center with edge class sample simply, all can cause recognition performance to descend.Poplar ten thousand buttons wait face identification method [poplar ten thousand buttons that proposed fuzzy maximum divergence difference differentiation (FMSD) and analyzed; Wang Jianguo; Ren Mingwu, Yang Jingyu. fuzzy contrary Fisher discriminatory analysis and the application in recognition of face [J] thereof. Chinese image graphics journal, 2009; 14 (1): 88-93.]; Through introducing fuzzy set theory each training sample is belonged to all kinds of samples with different extent, utilize the degree of membership information of sample to define the class average and the divergence matrix of sample again, the classification information that makes full use of known sample helps the feature extraction of classifying.Though FMSD has done effective improvement to edge class in the recognition of face and hard classification problem, can not extract the nonlinear organization characteristic of people's face effectively and realize linear separability.KMSD [the Wang JG that Wang etc. propose; Lin YS; Yang WK, et al. Kernel maximum scatter difference based feature extraction and its application to face recognition [J]. Pattern Recognition Letters, 2008; 29 (13): 1832-1835.] utilize the advantage of kernel method and maximum divergence difference sorter to realize the identification of people's face; Can extract the nonlinear organization characteristic of people's face effectively and reduce calculated amount, however said method all need to convert image array into image vector, can not effectively extract the structural information in the facial image.In order to obtain the structural information in the image; Hui Kong etc. has proposed two-dimensional nucleus principal component analysis (PCA) (K2DPCA) method [Hui Kong; Lei Wang, Eam Khwang Teoh, et al.Generalized 2D principal component analysis for face image representation and recognition [J]. Neural Networks; 2005; 18:585-594.], though can extract the structural information of people's face effectively, but can not effectively solve extraneous factors such as receiving illumination because of people's face sample and change edge class problem and the hard classification problem that produces; And Wang Jianguo etc. have proposed face identification method (2DPCA+2DMSD) [the Wang Jianguo based on two-way maximum divergence difference discriminatory analysis (Two-directional maximum scatter difference discriminant analysis); Yang Wankou; Lin Yusheng; Yang Jingyu. Two-directional maximum scatter difference discriminant analysis for face recognition [J]. Neurocomputing; 2008,72 (1-3): 352-358.], it at first utilizes the 2DPCA method that original image is projected in the lower dimensional space; Removed the correlativity between the image line effectively; Utilize maximum divergence difference method to extract effective diagnostic characteristics that facial image lists then, reduced the dimension of people's face diagnostic characteristics, realize the Classification and Identification fast and effeciently of people's face.Though the face identification method based on the discriminatory analysis of two-way maximum divergence difference has obtained recognition of face effect preferably, can not extract the nonlinear organization characteristic and solution edge class problem and hard classification problem of people's face effectively.
Summary of the invention
In recognition of face, can not extract the non-linear diagnostic characteristics and the problem that has edge class and hard classification of people's face effectively to two-way maximum divergence difference discriminant analysis method, the present invention has provided based on the method for two-dimensional nucleus principal component analysis (PCA) with the recognition of face of fuzzy maximum divergence difference.At first utilize the K2DPCA method to extract the nonlinear organization characteristic of people's face; Next is chosen, and to meet between class scatter after the projection be optimum axis of projection greater than the proper vector of divergence in the class; Use the FMSD method of discrimination then, the original distribution information of sample is dissolved in the feature extraction of people's face fully according to membership function; Adopt nearest neighbor classifier to carry out Classification and Identification at last.Specifically comprise:
The image collection
of 1, be provided with
type
individual face training sample; And
;
be
type
individual sample image wherein; The number of
type training sample
that is
;
is the sum of training sample;
is mapped to the nuclear sample matrix in the high-dimensional feature space
for sample
through non-linear transform function
;
is the corresponding average of
last
type sample;
is
goes up the corresponding average of all training samples,
prior probability of type sample that is
.
2, the divergence matrix does in the sample class of definition sample in high-dimensional feature space
3, the sample between class scatter matrix of definition sample in high-dimensional feature space
does
4, the sample total volume divergence matrix of definition sample in high-dimensional feature space
does
5, the two-dimentional principal component analysis (PCA) criterion function on the high-dimensional feature space
does
6, optimum axis of projection and optimum projector space
Separate the optimum projector space
on the pairing mutually orthogonal proper vector of preceding
individual relatively large eigenwert
formation
that formula (4) can obtain
, and
unties
.Because the concrete form of function
is unknown, can't directly obtain the solution space
of formula (4).Can know according to theory of reproducing kernel space; In
; The solution space
of all nuclear learning methods can be expressed as
inner product sum in feature space, that is:
Therefore; In order to obtain projector space
, a demand goes out
.For the major component of test sample book
in extracting
, a demand goes out that
gets final product at
upslide movie queen's sample characteristics matrix
in
.
(8)
Therefore with formula (4) high-dimensional feature space
of equal value in two-dimentional principal component analysis (PCA) criterion function following:
Preceding
individual relatively large nonzero eigenvalue that will be obtained
by separating of formula (10) and corresponding proper vector
are as optimum axis of projection; Constitute optimum projector space
by optimum axis of projection, and
.From equation (5) to obtain
human face samples optimal projection space
.
7, the image
with arbitrary sample projects to optimum projector space
; Obtain respective sample eigenmatrix
, constitute new sample space thus for
:
Utilize the FMSD method directly
to be carried out feature extraction.As training sample set, it is following to obtain corresponding membership function through fuzzy
neighbour's criterion with
:
Where
indicates sample
of
nearest neighbors of the sample belongs to the first
class number of samples.
9, calculate sample average
10, calculate the dimensionality reduction samples
corresponding class scatter matrix
and within-class scatter matrix
According to membership function and sample average; Can obtain a corresponding between class scatter matrix
of sample
and a type interior divergence matrix
behind the dimensionality reduction, they are respectively:
11, it is following to obtain corresponding maximum divergence difference criterion by a between class scatter matrix (formula 14) and a type interior divergence matrix (formula 15):
12, can obtain the optimum projector space
of characteristic value collection
and characteristic of correspondence vector
formation by separating of maximum divergence difference criterion (formula 16), then
13, all samples are projected onto the optimal projection space
and
in the characteristics of the sample matrix is obtained:
14, people's face training sample
is projected to the optimum diagnostic characteristics matrix
that optimum projector space
and
obtain respective sample, that is:
Then any samples are projected onto the optimal projection space
and
get samples to identify the optimal feature matrix are
-dimensional matrix;
15, so that
means
Personal Facial training set first
The first characteristic matrix samples
column
-dimensional column vector.Therefore the optimum diagnostic characteristics matrix of any two samples can be expressed as
and
, and the distance between the sample is:
16, will any one test sample
projected onto the optimal projection space
and
get the corresponding optimal identification of the characteristic matrix
.
17, according to nearest neighbor classifier people's face is carried out Classification and Identification.When satisfied
and
belong to
type sample, test sample book
is
type facial image.
Technique effect of the present invention: at first utilize the K2DPCA method to extract the nonlinear organization characteristic of people's face; Next is chosen the between class scatter that can make after the projection and constitutes optimum projector space
greater than the axis of projection of divergence in the class; So that the identification of people's face; And, remove the correlativity between the image line with the sample
that obtains after original sample
projects to
behind the dimensionality reduction; Then in space
; Utilize fuzzy maximum divergence difference method of discrimination directly to extract the diagnostic characteristics of facial image, remove the correlativity between the image column; Adopt nearest neighbor classifier to carry out Classification and Identification at last.This method has been concentrated the superiority of two-dimensional nucleus principal component analysis (PCA) with fuzzy maximum divergence difference sorter; Through introducing fuzzy theory; Again define the divergence matrix of sample; The degree of membership information of sample is dissolved in the feature extraction of people's face fully, has been carried out effective improvement to non-linear diagnostic characteristics that in recognition of face, can not extract people's face based on two-way maximum divergence difference discriminant analysis method effectively and the problem that has edge class and hard classification.
Description of drawings
Fig. 1 is one type of people's face sample image in the ORL face database.
Fig. 2 is one type of sample image in the YALE face database.
Fig. 3 sample distribution figure, wherein a is that the interval of two types of samples exists and to comprise and involved relation, b be the common factor of two types of sample intervals for empty, c be the common factor of two types of sample intervals for empty, d is the distribution plan of three types of samples.
Embodiment
Through concrete enforcement technical scheme of the present invention and effect are done further to describe below.
1,, will on facial image database ORL and YALE, carry out the contrast experiment respectively based on the face identification method of the face identification method (2DKFMSD) of two-dimensional nucleus principal component analysis (PCA) and fuzzy maximum divergence difference and 2DMSD, 2DFLD, 2DPCA+2DFLD, 2DPCA+2DMSD, KMSD in order to verify validity of the present invention.
2, (http://www.uk.research.att. com/facedatabase.html) has 40 people to the ORL facial image database; Everyone 10; By resolution be 112
people's face gray level image of 92 forms, comprising: different times (1992-1994), different expression and facial detail (open eyes/close one's eyes, laugh at/do not laugh at, the wear a pair of spectacles of wear a pair of spectacles/not), degree of depth rotation image with plane rotation (can reach 20 degree), dimensional variation (rate of change is 10%).
3, YALE facial image database (http://cvc.yale.edu/projects/ yalefaces/yalefaces.html) has 15 people; Everyone have 11 resolution be 243
320 image, comprising: different expressions and different facial detail (surprised/sleepiness/nictation/happiness/grief), lighting angle (left/in/right side) and the gray level image of wearing glasses/not wearing glasses.
4, in order to let experimental result have objectivity and comparability; This paper chooses
individual facial image at random as training sample from every type of sample storehouse, the residue sample is tested.
5, in order to reduce calculated amount, with the picture size in ORL and the YALE facial image database be normalized to respectively 56
46 and 40
50.
6, getting kernel function is gaussian kernel function:
; Wherein
adopts nearest neighbor classifier to classify.
7, in order under the situation of different characteristic dimension and identical number of training, to obtain average correct recognition rata, the value of parameter in the maximum divergence difference criterion function
all is taken as 1.
8, shown in accompanying drawing 1 and the accompanying drawing 2 be someone image in ORL and the YALE face database respectively.
9, be respectively the average recognition rate of distinct methods different training number of samples on ORL, YALE face database shown in subordinate list 1 and the subordinate list 3, therefrom be not difficult to find out, the 2DKFMSD method on the overall performance of recognition of face than KMSD and 2DPCA+2DMSD
]Stable more more efficient etc. method.Main cause is: 2DKFMSD utilizes the K2DPCA method that sample is mapped in the high-dimensional feature space; Is optimum axis of projection through choosing the between class scatter that meets after the projection greater than the proper vector of divergence in the class; Not only avoided coming a balance between class scatter and a type interior divergence through asking for suitable
value; Thereby realize the problem of the linear separation between people's face foreign peoples sample; And can also extract the nonlinear characteristic of people's face effectively, reduce the dimension of sample characteristics matrix.
10, accompanying drawing 3 is depicted as sample distribution figure; The distribution of sample in the space in the blue round dot representation class 1; The distribution of sample in the space in the red square representation class 2, the distribution of sample in the space of purple dot representation class 3, the class center of blue hollow ring representation class 1 sample; The class center of red hollow rectangle representation class 2 samples, the class center of purple hollow ring representation class 3 samples.
11, the existence of the interval of 1: two type of sample of situation comprises and involved relation, shown in accompanying drawing 3 (a), works as test sample book
C(sample
CSample in actual type of belonging to 2) when the distance of two types of center of a sample equates; If utilize nearest neighbor classifier to discern; Because from the sample of nearest sample point type of belonging to 1 of this test sample book, so mistake identification this moment appearred in this test sample book type of belonging to 1 sample.
12, the common factor of 2: two types of sample intervals of situation is empty, shown in accompanying drawing 3 (b), works as test sample book
CThe distance that is positioned at two types of sample class centers equates, and when divergence was unequal in the class of two types of samples, if utilize nearest neighbor classifier to discern, the probability of the sample that then divergence is bigger in this test sample book type of belonging to was big, the same identification problem by mistake that exists,
13, the common factor of 3: two types of sample intervals of situation is not empty, and shown in accompanying drawing 3 (c), the midpoint when class 1 center and type 2 centers has the test sample book of existence
C(test sample book
CThe sample of actual type of belonging to 1), like divergence in the class of fruit 1 sample during greater than the between class scatter of class 1 sample and type 2 samples, if utilize nearest neighbor classifier to classify, because test sample book
CLittler than it and the distance of sample 2 in type 1 with the distance of sample 1 in the class 2, then the sample in this test sample book type of belonging to 2 mistake also occurred and discerned this moment.
14, in order to address this problem; Visual recognition according to the people; Difference between the similar sample (divergence is represented in type) is inevitable less than the difference between foreign peoples's sample (between class scatter is represented), therefore chooses between class scatter greater than divergence in the class, meets human visual recognition rule.
15, shown in accompanying drawing 3 (a); When choose between class scatter after meeting projection greater than class in the proper vector of divergence when being optimum axis of projection; Lose the main information of the ability presentation video that utilizes the extraction of KPCA method rather than the axis of projection of authentication information, reduced the dimension of sample characteristics matrix effectively.
16, being directed against the mistake identification problem that exists in the accompanying drawing 3 (a), is to cause owing to part in the class 1 receives extraneous factor to influence bigger edge class sample on the one hand; Be because traditional MSD method utilizes
value to carry out the deficiency that concerns between balance between class scatter and type interior divergence on the other hand; MSD is enough little with respect between class scatter through divergence in adjustment
value type of making, so that classification.When in type of dwindling during divergence, be equivalent to all sample points and draw close, and utilize the inaccuracy at the class center that small sample calculates to cause the decline of discrimination to the class center of sample.
17, be worth so that the deficiency of classification to the edge class problem in the recognition of face of small sample with through adjustment
; The present invention is through the FMSD method;
value is made as 1; The space distribution information of sample is dissolved in the diagnostic characteristics extraction of facial image, has been improved the discrimination of people's face effectively.
18, shown in accompanying drawing 3 (d); Calculate the degree of membership information of sample 1; During the space distribution information of type of the belonging to 2 samples diagnostic characteristics of being dissolved into facial image does not extract with sample 3 grades; Avoided the hard classification problem that exists in the recognition of face effectively, the class center of type of calculating 2 samples is more accurate like this.
19, shown in accompanying drawing 3 (a); In four nearest samples of test specimen C, there are 3 to be type of belonging to 2 and 1 class 1 composition of sample; Leave five nearest samples of sample C by 4 classes 2 and 1 class 1 composition of sample; Utilize FMSD can extract the space distribution information of this sample effectively, the degree of membership of sample C type of belonging to 2 is respectively (
;
).
20, shown in accompanying drawing 3 (c), the degree of membership of test specimen C type of belonging to 1 is (
;
), therefore utilize optimum axis of projection that the FMSD method obtains to classify effectively less than the test sample book under the divergence situation in the class to the accompanying drawing 3 (a) and (c) between class scatter of middle sample.
21, the 2DKFMSD method at first utilizes KPCA to extract the capable authentication information of facial image; Extract the row authentication information of facial image then with the FMSD method; Take into account the authentication information of image row and column effectively; Not only reduce the optimum diagnostic characteristics space dimensionality of sample, and improved edge class and the hard classification problem that exists in the recognition of face effectively.
22, shown in subordinate list 2 and the subordinate list 4 be optimal identification rate, feature axis number and the recognition time table of comparisons of distinct methods distinct methods on ORL, YALE face database respectively.
23, the experimental data from subordinate list 2 and subordinate list 4 can be explained well: when people's face number of training is 5; The 2DKFMSD method has certain minimizing on recognition efficiency, recognition time (being the sample classification time), identification T.T. (comprising feature extraction time and sample classification time) and feature axis number (i.e. the minimum dimension of the sample characteristics matrix that obtains after the process feature extraction); Be fit to large-scale people's face sample identification more, as shown in Figure 1.
The average recognition rate of subordinate list 1 different training number of samples on the ORL face database
Table 1 The average recognition rate with different number of training sample On the ORL face image database
Like Fig. 2 is one type of sample image in the YALE face database.
Figure 2 A class of sample images in the YALE face image database
The average recognition rate of subordinate list 3 different training number of samples on YALE
Table 3 The average recognition rate with different number of training sample On the YALE face image database
Optimal identification rate, feature axis number and the recognition time table of comparisons of subordinate list 4 distinct methods on YALE
Table 4 The control table of different methods for optimal recognition rate, the number of characteristic shaft and recognition time on the YALE face image database
Claims (6)
1. one kind based on the face identification method of two-dimensional nucleus principal component analysis (PCA) with fuzzy maximum divergence difference, it is characterized in that method step is:
1.1, based on the optimum projector space model construction method of two-dimensional nucleus principal component analysis (PCA) and the fuzzy maximum divergence difference of two dimension;
1.2, based on people's face sample dimension reduction method of optimum projector space model;
1.3, based on people's face sample characteristics method for distilling of optimum projector space model;
1.4, based on kernel method the sample image matrix is mapped in the higher dimensional space, the demand not set up is separated the fuzzy maximum divergence difference sorter of the parameter model
in the maximum divergence difference sorter;
1.5, carry out the step of people's face Classification and Identification based on the face identification method (2DKFMSD) of two-dimensional nucleus principal component analysis (PCA) (K2DPCA) and fuzzy maximum divergence poor (FMSD).
2. according to claim 1 based on the face identification method of two-dimensional nucleus principal component analysis (PCA) with fuzzy maximum divergence difference; It is characterized in that described optimum projector space model construction method, comprise the steps: based on two-dimensional nucleus principal component analysis (PCA) and the fuzzy maximum divergence difference of two dimension
2.1, the image collection
of be provided with
type
individual face training sample; And
;
be
type
individual sample image wherein; The number of
type training sample
that is
;
is the sum of training sample;
is mapped to the nuclear sample matrix in the high-dimensional feature space
for sample
through non-linear transform function
;
is the corresponding average of
last
type sample;
is
goes up the corresponding average of all training samples,
prior probability of type sample that is
;
2.2, the divergence matrix does in the definition sample class of sample in high-dimensional feature space
(1)
2.3, the definition sample between class scatter matrix of sample in high-dimensional feature space
do
2.4, the definition sample total volume divergence matrix of sample in high-dimensional feature space
do
2.5, the two-dimentional principal component analysis (PCA) criterion function on the high-dimensional feature space
does
2.6, optimum axis of projection and optimum projector space
Separate the optimum projector space
on the pairing mutually orthogonal proper vector of preceding
individual relatively large eigenwert
formation
that formula (4) can obtain
; And
satisfies
; Because the concrete form of function
is unknown; Can't directly obtain the solution space
of formula (4); Can know according to theory of reproducing kernel space; In
; The solution space
of all nuclear learning methods can be expressed as
inner product sum in feature space, that is:
(5)
Therefore; In order to obtain projector space
; Demand goes out
; For the major component of test sample book
in extracting
, a demand goes out in
and Ji Kes at
upslide movie queen's sample characteristics matrix
;
Therefore with formula (4) high-dimensional feature space
of equal value in two-dimentional principal component analysis (PCA) criterion function following:
(10)
Preceding
individual relatively large nonzero eigenvalue that will be obtained
by separating of formula (10) and corresponding proper vector
are as optimum axis of projection; Constitute optimum projector space
by optimum axis of projection; And
is then by the optimum projector space
of people's face sample during formula (5) obtains
.
3. according to claim 1 based on the face identification method of two-dimensional nucleus principal component analysis (PCA) with fuzzy maximum divergence difference; It is characterized in that described people's face sample dimension reduction method based on optimum projector space model; The building method of its new samples space for
is following: the image
of arbitrary sample is projected to optimum projector space
; Obtain respective sample eigenmatrix
, constitute new sample space thus for
:
(11)。
4. the face identification method based on two-dimensional nucleus principal component analysis (PCA) and fuzzy maximum divergence difference according to claim 1 is characterized in that said people's face sample characteristics method for distilling based on optimum projector space model, comprises the steps:
Utilize the FMSD method directly
to be carried out feature extraction; As training sample set, it is following to obtain corresponding membership function through fuzzy
neighbour's criterion with
:
Where
indicates sample
of
nearest neighbors of the sample belongs to the first
class number of samples;
4.2, calculate sample average
4.3, calculate the dimensionality reduction samples
corresponding between-class scatter matrix
and within-class scatter matrix
According to membership function and sample average; Can obtain a corresponding between class scatter matrix
of sample
and a type interior divergence matrix
behind the dimensionality reduction, they are respectively:
(14)
(15)
5. according to claim 1 based on the face identification method of two-dimensional nucleus principal component analysis (PCA) with fuzzy maximum divergence difference; It is characterized in that saidly the sample image matrix being mapped in the higher dimensional space based on kernel method; The demand not set up is separated the fuzzy maximum divergence difference sorter of the parameter model
in the maximum divergence difference sorter, comprises the steps:
5.1, by between class scatter matrix (formula 14) and type in a divergence matrix (formula 15) to obtain corresponding maximum divergence difference criterion following:
5.2, can obtain the optimum projector space
that characteristic value collection
and characteristic of correspondence vector
constitutes by separating of maximum divergence difference criterion (formula 16), then
5.3, all samples are projected onto the optimal projection space
and
in the characteristics of the sample matrix is obtained:
(18)。
6. according to claim 1 based on the face identification method of two-dimensional nucleus principal component analysis (PCA) with fuzzy maximum divergence difference, it is characterized in that based on two-dimensional nucleus principal component analysis (PCA) (K2DPCA) following with the step that the face identification method (2DKFMSD) that blurs maximum divergence poor (FMSD) carries out people's face Classification and Identification:
6.1, people's face training sample
is projected to the optimum diagnostic characteristics matrix
that optimum projector space
and
obtain respective sample, that is:
Then any samples are projected onto the optimal projection space
and
get samples to identify the optimal feature matrix are
-dimensional matrix;
6.2, expression
the individual face training sample that makes
concentrates
row
dimensional vector of
individual sample characteristics matrix; Therefore the optimum diagnostic characteristics matrix of any two samples can be expressed as
and
, and the distance between the sample is:
6.3, will any one test sample
projected onto the optimal projection space
and
get the corresponding optimal identification of the characteristic matrix
;
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CN109872190A (en) * | 2019-02-20 | 2019-06-11 | 北京亿百分科技有限公司 | Based on LDA model extraction consumer to the method for advertising campaign interest-degree |
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