Fuzzy maximal marginal embedding and its application

In this paper, we develops a new approach, called fuzzy maximal marginal embedding (FMME), combining LMME (local maximal marginal embedding) with fuzzy set theory, in which the fuzzy k-nearest neighbor (FKNN) is implemented to achieve the nature distribution information of original samples, and this information is utilized to redefine the affinity weights of neighborhood graph (intraclass and interclass ) instead of the weights of the binary pattern. We can reduce sensitivity of the method to substantial variations between samples caused by varying illumination and shape, viewing conditions. That makes FMME more powerful and robust than other method. The proposed algorithm is examined using Yale and ORL face image databases. The experimental results show FMME outperforms PCA, LDA, LPP and LMME.

[1]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[3]  Hua Yu,et al.  A direct LDA algorithm for high-dimensional data - with application to face recognition , 2001, Pattern Recognit..

[4]  Witold Pedrycz,et al.  Face recognition using a fuzzy fisherface classifier , 2005, Pattern Recognit..

[5]  Jian Yang,et al.  Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  George Loizou,et al.  Computer vision and pattern recognition , 2007, Int. J. Comput. Math..

[7]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Honggang Zhang,et al.  Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics" , 2007, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Aziz Umit Batur,et al.  Linear subspaces for illumination robust face recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Jieping Ye,et al.  An optimization criterion for generalized discriminant analysis on undersampled problems , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  I. Jolliffe.  Principal Component Analysis , 2002 .

[12]  Zhihui Lai,et al.  Local Maximal Marginal Embedding with Application to Face Recognition , 2008, 2008 Chinese Conference on Pattern Recognition.

[13]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..