Improved-LDA based face recognition using both facial global and local information

To achieving higher classification rate under various conditions is challenging task in face recognition community. This paper presents a combined feature Fisher classifier (CF^2C) approach for face recognition, which is robust to moderate changes of illumination, pose and facial expression. The novelty of the method are: (1) the facial combined feature used for face representation, which is derived from facial global and local information extracted by DCT and (2) the development of Fisher classifier for high-dimensional multi-classes problem. Experiments on ORL and Yale face databases show that the proposed approach is superior to the traditional methods such as Eigenfaces and Fisherfaces.

[1]  Ravi Kothari,et al.  Fractional-Step Dimensionality Reduction , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  P. Yip,et al.  Discrete Cosine Transform: Algorithms, Advantages, Applications , 1990 .

[3]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[4]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

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

[6]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[7]  David Zhang,et al.  A face and palmprint recognition approach based on discriminant DCT feature extraction , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  Pong C. Yuen,et al.  Multi-cues eye detection on gray intensity image , 2001, Pattern Recognit..

[9]  Alex Pentland,et al.  Probabilistic Visual Learning for Object Representation , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Xin Yang,et al.  Face Recognition Using Improved-LDA , 2004, ICIAR.

[11]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[12]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[13]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[14]  K. Etemad,et al.  Discriminant analysis for recognition of human face images , 1997 .

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

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

[17]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Robert P. W. Duin,et al.  Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Martin D. Levine,et al.  Face Recognition Using the Discrete Cosine Transform , 2001, International Journal of Computer Vision.

[21]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Gita Sukthankar,et al.  Face Recognition: A Critical Look at Biologically-Inspired Approaches , 2000 .

[23]  Chengjun Liu,et al.  Enhanced Fisher linear discriminant models for face recognition , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).