Title :
Bilateral Two Dimensional Linear Discriminant Analysis for Stereo Face Recognition
Author :
Wang, Jian-Gang ; Kong, Hui ; Yau, Wei-Yun
Author_Institution :
Inst. for Infocomm Res., Singapore
Abstract :
A new method called two-dimensional Fisher discriminant analysis (2D-FDA) is proposed to deal with the small sample size (SSS) problem in LDA based face recognition. Then appearance and depth information are combined to improve face recognition rate. Different from the conventional 1D-FDA (PCA plus LDA) approaches, 2D-FDA is based on 2D image matrices rather than column vectors so the image matrix does not need to be transformed into a long vector before feature extraction. The advantage arising in this way is that the SSS problem does not exist any more because the between-class and within-class scatter matrices constructed in 2D-FDA are both of full-rank. It was verified that 2D-FDA outperforms 1D FDA
Keywords :
S-matrix theory; face recognition; feature extraction; image sampling; 2D Fisher discriminant analysis; 2D image matrix; appearance information; bilateral 2D linear discriminant analysis; depth information; feature extraction; scatter matrices; small sample size problem; stereo face recognition; Bagging; Data mining; Eigenvalues and eigenfunctions; Face recognition; Feature extraction; Functional analysis; Linear discriminant analysis; Null space; Principal component analysis; Scattering;
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2521-0
DOI :
10.1109/ICPR.2006.324