1. BDPCA Plus LDA: A Novel Fast Feature Extraction Technique for Face Recognition 授課教授 : 連震杰 老師 組員 : 黃彥綸 何域禎 W. Zuo, D. Zhang, J. Yang, K. Wang, “BBPCA plus LDA: a novel fast feauture extraction technique for face recognition,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 36, no. 4, pp. 946-953, Aug. 2006.

2. Outline Introduction Principal Component Analysis (PCA) Bidirectional Principal Component Analysis(BDPCA) Image Reconstruction BDPCA plus LDA Technique Experiments

3. Introduction Geometric-based approaches  Feature detection  High recognition rate.  Feature location is difference from people to people.

4. Introduction Holistic-based approaches  Robust recognition performance under noise, blurring, and partial occlusion.  EX:PCA(extract eigenfaces) 、 LDA(has SSS problem)

5. Q&A Q:What is small sample size (SSS) problem? A:In LDA the rank of Sw must be, then exist. For example, the ORL database image is 112x92 size, Sw :(112x92)x(112x92), and has 40 classifications, it must has 10344 pictures for training, but there is no a so large database.

6. Q&A Q:How to solve the SSS problem? A:Before using LDA, we first do image dimensionality reduction, such as PCA, BDPCA.

7. PCA Feature extraction:eigenfaces method Data compression Image dimensionality reduction Fail to classfication=>LDA

8. Flowchart Find LDA projector Mapping data to BDPCA subspace Mapping data to LDA subspace KNN to get the face recognition rate TestingTraining Mapping data to LDA subspace Mapping data to BDPCA subspace Find BDPCA projector Image dimensionality reduction

9. BDPCA Bidirectional PCA(BDPCA) row projection matrix column projection matrix Y: Feature matrix

10. Image Reconstruction PCABDPCAOriginal image Training Testing

11. MSE curves TrainingTesting

12. BDPCA plus LDA Technique Generalized eigendecomposition Mapping Y into its 1D representation y Between-class scatter matrix of y Within-class scatter matrix of y The LDA projector The final feature vector

13. Advantage PCA mxn (mxn)x1 (mxn)x(mxn) BDPCA mxn nxnmxm Y

14. CPU time On ORL face database

15. Experiments To test the efficacy of BDPCA + LDA, we make use of two face databases, the ORL face database and the FERET database. Since our aim is to evaluate the efficacy of feature extraction methods, we use a simple classifier, the nearest neighbor classifier.

16. Comparisons of the recognition rates ORL databaseFERET database

17. Conclusions The BDPCA +LDA has a much faster speed for facial feature extraction. The BDPCA + LDA needs less memory requirement because its projector is much smaller than that of the PCA + LDA. The BDPCA + LDA has a higher recognition accuracy over the PCA + LDA.