Patch-based Gabor Fisher Classifier for Face Recognition

1. 1 Patch-based Gabor Fisher Classifier for Face Recognition Yu Su, Shiguang Shan, Xilin Chen, Wen Gao ICT-ISVISION Joint R&D Lab for Face Recognition, Institute of Computing Technology, CAS, China 1. Hello everyone. Welcome to my presentation. 2. My name is Xiujuan Chai. 3. The title of my presentation is Local Linear Regression (LLR) for Pose Invariant Face Recognition. 1. Hello everyone. Welcome to my presentation. 2. My name is Xiujuan Chai. 3. The title of my presentation is Local Linear Regression (LLR) for Pose Invariant Face Recognition.

2. 2 Outline Previous Works Motivation & Basic Idea Proposed Method Experiments Summary This is the outline of my presentation. First I will talk about the pose problem and the related previous works. Then, I will give the motivation and the basic idea of our method. The concrete local linear regression method will be presented later and followed with some experimental results. In the last, I will give the summary.This is the outline of my presentation. First I will talk about the pose problem and the related previous works. Then, I will give the motivation and the basic idea of our method. The concrete local linear regression method will be presented later and followed with some experimental results. In the last, I will give the summary.

3. 3 Previous Works Gabor based methods has become dominative methods in face recognition Gabor provides multi-scale, multi-orientation local features Typical Gabor based methods for face recognition Elastic Bunch Graph Matching (EBGM) * Pre-assigned landmarks. People probably recognize face differently from machine It highly depends on the accuracy of landmark locations

4. 4 Previous Works Gabor based methods has become dominative methods in face recognition Gabor provides multi-scale, multi-orientation local features Typical Gabor based methods for face recognition Elastic Bunch Graph Matching (EBGM) Gabor Fisher Classifier (GFC) * Pre-assigned uniformed mesh Higher complexity

5. 5 Previous Works Gabor based methods has become dominative methods in face recognition Gabor provides multi-scale, multi-orientation local features Typical Gabor based methods for face recognition Elastic Bunch Graph Matching (EBGM) Gabor Fisher Classifier (GFC) AdaBoosted Gabor Fisher Classifier (AGFC) * Using AdaBoost to select Gabor features Only those most discriminative Gabor features are kept for further classification

6. 6 Problems Holistic representation have three disadvantages Lost of the spatial information of features Suppose of the identical discriminative capacity of the features Too large image variation to be modeled by linear subspace

7. 7 Outline Previous Works Motivation & Basic Idea Proposed Method Experiments Summary This is the outline of my presentation. First I will talk about the pose problem and the related previous works. Then, I will give the motivation and the basic idea of our method. The concrete local linear regression method will be presented later and followed with some experimental results. In the last, I will give the summary.This is the outline of my presentation. First I will talk about the pose problem and the related previous works. Then, I will give the motivation and the basic idea of our method. The concrete local linear regression method will be presented later and followed with some experimental results. In the last, I will give the summary.

8. 8 Motivation & Our Solution Motivation Discriminative patches are more efficient than the whole face image Some trivial parts will decrease the discriminative capability of the feature Even more, some extrinsic variances provide wrong information for further classification Our solution Partition the face image, and use the discriminative patches only

9. 9 Proposed Method Patch-based Gabor Fisher Classifier Partition face image into several discriminative patches by certain learning process. Get multiple Gabor feature segments by concatenating all the features within the corresponding patch. Design a FDA (Fisher Discriminant Analysis) classifier based on each feature segment. Combine all these component FDA classifiers.

10. 10 Learning discriminating patches Use AdaBoost to select image patches AdaBoost is a powerful feature selection method. Convert multi-class problem to two-class problem. Intra-personal difference Extra-Personal difference Candidate patches set is formed by exhaustively enumerating the spatial position and patch size. Patches are considered as features which can be selected one by one from the candidate set.

11. 11 Train and combine component GFCs Train multiple GFCs by applying FDA on each selected patch. Combine all the component GFCs by sum rule.

12. 12 Outline Previous Works Motivation & Basic Idea Proposed Method Experiments Summary This is the outline of my presentation. First I will talk about the pose problem and the related previous works. Then, I will give the motivation and the basic idea of our method. The concrete local linear regression method will be presented later and followed with some experimental results. In the last, I will give the summary.This is the outline of my presentation. First I will talk about the pose problem and the related previous works. Then, I will give the motivation and the basic idea of our method. The concrete local linear regression method will be presented later and followed with some experimental results. In the last, I will give the summary.

13. 13 Experiments Proposed method (PGFC) vs. GFC & AGFC Database FERET CAS-PEAL-R1 Some parameters Image size : 64 X 64 Gabor feature : 5 scales, 8 orientations Feature dimension GFC : 10240 Gabor features AGFC : about 3000 Gabor features PGFC : up to 100 Gabor patches Note that the patch size is restricted within 32 by 32 to avoid the problems of holistic representation.

14. 14 Experiments on FERET Training set Standard FERET training set (1002 images from 429 subjects) is used to learn the most discriminant patches and train FDA classifiers. Gallery 1196 subjects with only 1 image / subject Probe sets fafb (1195 subjects, 1 image / subject, expression) fafc (194 subjects, 1 image / subject, light) Dup1 (243 subjects, 722 images, aging, less than 3 years ) Dup2 (75 subjects, 234 images, aging, more than 1 year later)

15. 15 Performance comparisons

16. 16 Performance comparisons (cont.)

17. 17 Experiments on CAS-PEAL-R1 Training set 1200 images from 300 subjects Gallery 1040 subjects, 1 image / subject Probe sets Accessory, 2646 images from 438 subjects Background, 650 images from 297 subjects Expression, 1884 images from 377 subjects Lighting, 2450 images from 233 subjects

18. 18 Performance comparisons

19. 19 Outline Previous Works Motivation & Basic Idea Proposed Method Experiments Summary This is the outline of my presentation. First I will talk about the pose problem and the related previous works. Then, I will give the motivation and the basic idea of our method. The concrete local linear regression method will be presented later and followed with some experimental results. In the last, I will give the summary.This is the outline of my presentation. First I will talk about the pose problem and the related previous works. Then, I will give the motivation and the basic idea of our method. The concrete local linear regression method will be presented later and followed with some experimental results. In the last, I will give the summary.

20. 20 Summary Proposed an effective patch-based method for face recognition The method overcomes some disadvantages in other Gabor-based methods: Considering the spatial information of Gabor features Considering the different discriminative capacity of Gabor features reducing the variation of face which is difficult to be model by linear subspace methods such as FDA Experiments on two large databases FERET and CAS-PEAL-R1 show that the proposed algorithm is more effective than GFC and AGFC.

21. 21 Thanks! That’s all, Thank you!That’s all, Thank you!