CN102495999A - Face recognition method - Google Patents
Face recognition method Download PDFInfo
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- CN102495999A CN102495999A CN2011103583624A CN201110358362A CN102495999A CN 102495999 A CN102495999 A CN 102495999A CN 2011103583624 A CN2011103583624 A CN 2011103583624A CN 201110358362 A CN201110358362 A CN 201110358362A CN 102495999 A CN102495999 A CN 102495999A
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Abstract
The invention provides a face recognition method, which is a three-dimensional face recognition algorithm by utilizing two-dimensional Gabor wavelet features for two-dimensional Gabor wavelet feature extraction based on kernel linear discrimination. Effective two-dimensional Gabor wavelet features of a face are combined with the three-dimensional face recognition algorithm, and feature vectors are analyzed by a linear discrimination analysis method, thus influence on the face recognition caused by factors including illumination, posture, expression and the like and disadvantages of single three-dimensional face recognition on the factor of extracting effective face features are overcome.
Description
Technical field
The present invention relates to the recognition of face field, particularly based on the three-dimensional face recognition algorithm of two-dimensional Gabor wavelet character.
Background technology
Recognition of face is the computer technology research field of a hot topic, and it belongs to biometrics identification technology, is that the biological characteristic of human body itself is distinguished the biosome individuality.The two-dimension human face recognition technology is ripe at present; But two-dimension human face identification quality receives following factor affecting: (1) illumination; Image by photo obtains is represented people's face looks with half-tone information, and under varying environment (light, background, visual angle etc.), the human face photo difference that obtains is very big; (2) how attitude, photo obtain 3 d pose and adjusts having difficulty greatly from 2-dimentional photo usually not in strict conformity with the front or the lateral attitude condition; (3) expression shape change of people's face and looks can have a strong impact on recognition result with the variation of age generation.The variation of illumination and attitude is the subject matter that recognition of face faces, same individual the facial image difference under different light and the attitude usually greater than different people same light according to and attitude under difference.Three-dimensional face identification is directly extracted people's face in three-dimensional real features information; Be different from the plane projection of people's face on certain direction in the two-dimension human face identification; Therefore; Its characteristic information is more abundant, can on original two-dimension human face base of recognition, effectively improve system identification degree of accuracy and the enhanced system robustness to attitude, illumination and expression.But people's face is a flexible article, and the flexibility of human face expression changes to the three-dimensional model influence very greatly.
The multiple small echo of two-dimensional Gabor is proposed by Daugman the earliest, is widely used in various Flame Image Process thereupon, has obtained extraordinary effect.
The multiple small echo of two-dimensional Gabor is:
Wherein,
;
;
and
is the scale parameter of wave filter;
expression be frequency,
expression be direction.
Formula (1) at frequency domain representation is:
At present, also less than three-dimensional face recognition algorithm based on the two-dimensional Gabor wavelet character.
Summary of the invention
The objective of the invention is to disclose a kind of three-dimensional face recognition algorithm of two-dimensional Gabor wavelet character.
The present invention in order to realize the technical scheme that its goal of the invention adopts is: a kind of face identification method, this method are a kind of three-dimensional face recognition algorithm based on the two-dimensional Gabor wavelet character, may further comprise the steps:
Steps A, align the dough figurine face and detect, locate crucial human face characteristic point in a front face and the facial image, obtain people's face original image I (x, y);
Step B, characterize about the Gabor of facial image through Gabor wavelet analysis obtain; Make described original image I (x, the individual features in y) is converted into Gabor proper vector
;
Step C, adopt little exponential polynomials (FPP) model
, m dimension Gabor feature space
is projected in the higher n dimension space
;
Step D, based on nuclear linear judgment analysis algorithm (KFDA); Matrix
in matrix
and the class between in
space, setting up type; Wherein,
, the standard orthogonal characteristic of calculating
vector
;
Step e, extraction facial image are significantly differentiated proper vector;
Step F, the inapparent differentiation proper vector of extraction facial image;
Step G, utilize facial image significantly to differentiate proper vector and the inapparent differentiation proper vector of facial image and a 3D face database reconstruction of three-dimensional faceform;
Step H, adopt template matches and linear discriminant analysis (FLDA) method to handle to described three-dimensional face model, difference is carried out the match people face with a type differences in the class of extraction model.
Further, in above-mentioned a kind of face identification method: in the described steps A, crucial human face characteristic point in the described facial image comprises contour feature point, left eye and right eye, mouth and the nose of people's face.
Further, in above-mentioned a kind of face identification method: comprise in the described step e:
Step e 01, make
; Wherein,
is that q the eigenwert that matrix
is corresponding in described type is positive proper vector,
;
Step e 02, calculate
corresponding to the proper vector
of
individual eigenvalue of maximum; Wherein,
;
;
Further, in above-mentioned a kind of face identification method: comprise in the described step F:
Step F01 calculation
? corresponds to a maximum eigenvalue vector
;
Further, in above-mentioned a kind of face identification method: the 3D face database is the single face three-dimensional face database of ORL (Olivetti Research Laboratory) among the described step G.
Further, in above-mentioned a kind of face identification method: among the described step G:
Step G01, confirm a Feature Conversion matrix P;
Step G02, the Gabor that extracts is differentiated the proper vector of proper vector corresponding to m eigenvalue of maximum, form main Feature Conversion matrix P ';
Step G03, extensive face database is carried out repetition training to obtain average face S.
The present invention proposes a kind of two-dimensional Gabor wavelet character based on the linear judgement of nuclear and extracts; Adopt the three-dimensional face recognition algorithm of two-dimensional Gabor wavelet character; Through people's face efficient 2-d Gabor wavelet character is combined with three-dimensional face recognition algorithm; Use the linear discriminant analysis method that proper vector is analyzed, overcome factors such as illumination, attitude and expression influence and the single three-dimensional face that recognition of face causes is identified in the deficiency of extracting effective face characteristic aspect.
Below in conjunction with specific embodiment the present invention is done comparatively detailed description.
Description of drawings
Fig. 1, process flow diagram of the present invention.
Fig. 2 is an embodiment of the invention design sketch.
Embodiment
As shown in Figure 1, present embodiment is a kind of three-dimensional face recognition algorithm based on the two-dimensional Gabor wavelet character, and this method is a kind of three-dimensional face recognition algorithm based on the two-dimensional Gabor wavelet character, may further comprise the steps:
Steps A, align the dough figurine face and detect, locate crucial human face characteristic point in a front face and the facial image, obtain people's face original image I (x, y); Crucial human face characteristic point in the image comprises contour feature point, left eye and right eye, mouth and the nose of people's face.
Step B, characterize about the Gabor of facial image through Gabor wavelet analysis obtain; Make described original image I (x, the individual features in y) is converted into Gabor proper vector
;
Step C, adopt little exponential polynomials (FPP) model
, m dimension Gabor feature space
is projected in the higher n dimension space
;
Step D, based on nuclear linear judgment analysis algorithm (KFDA); Matrix
in matrix
and the class between in
space, setting up type; Wherein,
, the standard orthogonal characteristic of calculating
vector
;
Step e, extraction facial image are significantly differentiated proper vector; Make
; Wherein,
is
, and q corresponding eigenwert is positive proper vector;
. calculating
is corresponding to the proper vector
of
individual eigenvalue of maximum; Wherein,
;
; Significantly differentiate proper vector
; Wherein,
Step F, extracted face image is not significant discriminant feature vector; calculation
? largest eigenvalue corresponds to an eigenvector
.Make
; Then inapparent differentiation proper vector
,
.
Step G, utilize facial image significantly to differentiate proper vector and the inapparent differentiation proper vector of facial image and a 3D face database reconstruction of three-dimensional faceform; In order to rebuild a three-dimensional face model, use the single face three-dimensional face database of ORL (Olivetti Research Laboratory).Confirm a Feature Conversion matrix P, in original three-dimensional face identification method, the subspace analysis projection matrix that this matrix is normally obtained by the subspace analysis method is made up of corresponding to the proper vector of a preceding m eigenvalue of maximum the covariance matrix of sample.The Gabor that extracts is differentiated the proper vector of proper vector corresponding to m eigenvalue of maximum; Form main Feature Conversion matrix P '; This Feature Conversion matrix has stronger robustness than original eigenmatrix P to factors such as illumination, attitude and expressions, and promptly the characteristic of representative is more accurate and stable.Extensive face database is carried out repetition training to obtain average face S.
Step H, adopt template matches and linear discriminant analysis (FLDA) method to handle to described three-dimensional face model, difference is carried out the match people face with a type differences in the class of extraction model.
Comparative study this paper method with based on the recognition technology of Gabor characteristic quantity and kernel function principal component analysis (PCA) (KPCA), compare based on Gabor characteristic quantity and principal component analytical method (PCA) recognition technology, the result shows that the method for this paper has improved people's ability.The result is as shown in Figure 2.
Claims (6)
1. face identification method, this method is a kind of three-dimensional face recognition algorithm based on the two-dimensional Gabor wavelet character, it is characterized in that: may further comprise the steps:
Steps A, align the dough figurine face and detect, locate crucial human face characteristic point in a front face and the facial image, obtain people's face original image I (x, y);
Step B, characterize about the Gabor of facial image through Gabor wavelet analysis obtain; Make described original image I (x, the individual features in y) is converted into Gabor proper vector
;
Step C, adopt little exponential polynomials (FPP) model
, m dimension Gabor feature space
is projected in the higher n dimension space
;
Step D, based on nuclear linear judgment analysis algorithm (KFDA); Matrix
in matrix
and the class between in
space, setting up type; Wherein,
, the standard orthogonal characteristic of calculating
vector
;
Step e, extraction facial image are significantly differentiated proper vector;
Step F, the inapparent differentiation proper vector of extraction facial image;
Step G, utilize facial image significantly to differentiate proper vector and the inapparent differentiation proper vector of facial image and a 3D face database reconstruction of three-dimensional faceform;
Step H, adopt template matches and linear discriminant analysis (FLDA) method to handle to described three-dimensional face model, difference is carried out the match people face with a type differences in the class of extraction model.
2. a kind of face identification method according to claim 1 is characterized in that: in the described steps A, crucial human face characteristic point in the described facial image comprises contour feature point, left eye and right eye, mouth and the nose of people's face.
3. a kind of face identification method according to claim 1 is characterized in that: comprise in the described step e:
Step e 01, make
; Wherein,
is that q the eigenwert that matrix
is corresponding in described type is positive proper vector,
;
Step e 02, calculate
corresponding to the proper vector
of
individual eigenvalue of maximum; Wherein,
;
;
5. a kind of face identification method according to claim 1 is characterized in that: the 3D face database is the single face three-dimensional face database of ORL (Olivetti Research Laboratory) among the described step G.
6. a kind of face identification method according to claim 5 is characterized in that: among the described step G:
Step G01, confirm a Feature Conversion matrix P;
Step G02, the Gabor that extracts is differentiated the proper vector of proper vector corresponding to m eigenvalue of maximum, form main Feature Conversion matrix P ';
Step G03, extensive face database is carried out repetition training to obtain average face S.
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Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103745208A (en) * | 2014-01-27 | 2014-04-23 | 中国科学院深圳先进技术研究院 | Face recognition method and device |
CN103745206A (en) * | 2014-01-27 | 2014-04-23 | 中国科学院深圳先进技术研究院 | Human face identification method and system |
CN103902992A (en) * | 2014-04-28 | 2014-07-02 | 珠海易胜电子技术有限公司 | Human face recognition method |
CN103971122A (en) * | 2014-04-30 | 2014-08-06 | 深圳市唯特视科技有限公司 | Three-dimensional human face description method and device based on depth image |
CN105678341A (en) * | 2016-02-19 | 2016-06-15 | 天纺标检测科技有限公司 | Wool cashmere recognition algorithm based on Gabor wavelet analysis |
WO2017107957A1 (en) * | 2015-12-22 | 2017-06-29 | 中兴通讯股份有限公司 | Human face image retrieval method and apparatus |
WO2020199693A1 (en) * | 2019-03-29 | 2020-10-08 | 中国科学院深圳先进技术研究院 | Large-pose face recognition method and apparatus, and device |
CN112784660A (en) * | 2019-11-01 | 2021-05-11 | 财团法人工业技术研究院 | Face image reconstruction method and system |
-
2011
- 2011-11-14 CN CN2011103583624A patent/CN102495999A/en active Pending
Non-Patent Citations (3)
Title |
---|
孔华锋等: "基于二维Gabor 小波特征的三维人脸识别算法", 《计算机工程》 * |
曹林等: "基于二维Gabor小波的人脸识别算法", 《电子与信息学报》 * |
王琳等: "基于二维Gabor 小波矩阵表征人脸的识别算法", 《计算机工程》 * |
Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103745206A (en) * | 2014-01-27 | 2014-04-23 | 中国科学院深圳先进技术研究院 | Human face identification method and system |
CN103745208A (en) * | 2014-01-27 | 2014-04-23 | 中国科学院深圳先进技术研究院 | Face recognition method and device |
CN103745206B (en) * | 2014-01-27 | 2018-07-10 | 中国科学院深圳先进技术研究院 | A kind of face identification method and system |
CN103902992B (en) * | 2014-04-28 | 2017-04-19 | 珠海易胜电子技术有限公司 | Human face recognition method |
CN103902992A (en) * | 2014-04-28 | 2014-07-02 | 珠海易胜电子技术有限公司 | Human face recognition method |
WO2015165227A1 (en) * | 2014-04-28 | 2015-11-05 | 珠海易胜电子技术有限公司 | Human face recognition method |
CN103971122A (en) * | 2014-04-30 | 2014-08-06 | 深圳市唯特视科技有限公司 | Three-dimensional human face description method and device based on depth image |
WO2017107957A1 (en) * | 2015-12-22 | 2017-06-29 | 中兴通讯股份有限公司 | Human face image retrieval method and apparatus |
CN105678341A (en) * | 2016-02-19 | 2016-06-15 | 天纺标检测科技有限公司 | Wool cashmere recognition algorithm based on Gabor wavelet analysis |
CN105678341B (en) * | 2016-02-19 | 2018-11-13 | 天纺标检测认证股份有限公司 | A kind of woollen and cashmere recognizer based on Gabor wavelet analysis |
WO2020199693A1 (en) * | 2019-03-29 | 2020-10-08 | 中国科学院深圳先进技术研究院 | Large-pose face recognition method and apparatus, and device |
CN112784660A (en) * | 2019-11-01 | 2021-05-11 | 财团法人工业技术研究院 | Face image reconstruction method and system |
CN112784660B (en) * | 2019-11-01 | 2023-10-24 | 财团法人工业技术研究院 | Face image reconstruction method and system |
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Application publication date: 20120613 |