Fisher's linear discriminant (FLD) and support vector machine (SVM) in non-negative matrix factorization (NMF) residual space for face recognition

Optica Applicata

Volumn 40, Issue 3, 2010, Pages 693-704

  Zhou, Changjun ^a   Wei, Xiaopeng ^a   Zhang, Qiang ^a   Fang, Xiaoyong ^a  

^a DALIAN UNIVERSITY   (China)

Author keywords

Face recognition; Fisher linear discriminant (FLD); Non negative matrix factorization (NMF); Residual image.

Indexed keywords

COEFFICIENT MATRIX; FACE IMAGES; FISHER LINEAR DISCRIMINANTS; FISHER'S LINEAR DISCRIMINANT; LOCAL FEATURE; NONNEGATIVE MATRIX FACTORIZATION; NOVEL METHODS; ORIGINAL IMAGES; ORL DATABASE; RECONSTRUCTED IMAGE; RESIDUAL IMAGES; RESIDUAL SPACES; YALE FACE DATABASE;

BLIND SOURCE SEPARATION; COMPUTER SIMULATION; FACE RECOGNITION; FACTORIZATION; FEATURE EXTRACTION; GEARS; SUPPORT VECTOR MACHINES; VECTOR SPACES; VECTORS;

MATRIX ALGEBRA;

EID: 78149448773     PISSN: 00785466     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (21)

1. Kirby M., Sirovich L., Application of the Karhunen-Loeve procedure for the characterization of human faces, IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (1), 1990, pp. 103-108.
2. Turk M.A., Pentland A.P., Eigenfaces for recognition, Journal of Cognitive Neuroscience 3(1), 1991, pp. 71-86.
3. Belhumeur P.N., Hespanha J.P., Kriegman D.J., Eigenfaces vs. Fisherfaces: recognition using class specific linear projection, IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (7), 1997, pp. 711-720.
4. An G.Y., Ruan Q.Q., A novel mathematical model for enhanced Fisher's linear discriminant and its application to face recognition, The 18th International Conference on Pattern Recognition, 2006.
5. Comon P., Independent component analysis. A new concept?, Signal Processing 36(3), 1994, pp. 287-314.
6. Lee D.D., Seung H.S., Learning the parts of objects by non-negative matrix factorization, Nature 401, 1999, pp. 788-791.
7. Wang Y., Jia Y.D., Hu C.B., Turk M., Fisher non-negative matrix factorization for learning local features, Asian Conference on Computer Vision, Korea, 2004.
8. Duda R.O., Hart P.E., Stork D.G., Pattern Classification, Wiley-Interscience, New York, 2001.
9. Kim T.K., Kim H., Hwang W., Kittler J., Independent component analysis in a facial local residue space, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recogniton, 2003, pp. 1063-1069.
10. Burges C.J.C., A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery 2 (2), 1998, pp. 121-167.
11. Cristianini N., Taylor J.S., An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, 2004, pp. 82-108.
12. Vapnik V.N., Statistical Learning Theory, Wiley, New York, 1998.
13. Pontil M., Verri A., Support vector machines for 3-D object recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (6), 1998, pp. 637-646.
14. Chen W., Meng Joo Er, Shiqian Wu, Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithmic domain, IEEE Transactions on Systems, Man, and Cybernetics, Part B 36 (2), 2006, pp. 458-466.
15. Xu Y.Q., Li B.C., Wang B., Face recognition by fast independent component analysis and genetic algorithm, Proceedings of the Fourth International Conference on Computer and Information Technology (CIT'04), 2004
16. Samaria F., Harter A., Parameterisation of a stochastic model for human face identification, 2nd IEEE Workshop on Applications of Computer Vision, Sarasota, FL, 1994.
17. Lee K.C., Ho J., Kriegman D.J., Acquiring linear subspaces for face recognition under variable lighting, IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (5), 2005, pp. 684-698.
18. Ma J., Zhao Y., Ahalt S., OSU SVM Classier Matlab Toolbox (ver 3.00), http://eewww.eng.ohiostate.edu/maj/osusvm, 2002.
19. Chang C.C., Lin C.J., LIBSVM: a library for support vector machines, http://www.csie.ntu.edu.tw/cjlin/libsvm, 2001.
20. Wang C.J., Zhang Q., Wei X.P., Zhou C.J., Face recognition based on NMF in a residual image, Proceedings of the 4th International Conference on Impulsive and Hybrid Dynamical Systems, 2007, pp. 2125-2128.
21. Georghiades A.S., Belhumeur P.N., Kriegman D.J., From few to many: Illumination cone models for face recognition under variable lighting and pose, IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (6), 2001, pp. 643-660.