Multilinear principal component analysis for face recognition with fewer features

Neurocomputing

Volumn 73, Issue 10-12, 2010, Pages 1550-1555

  Wang, Jin ^a   Barreto, Armando ^a   Wang, Lu ^a   Chen, Yu ^a   Rishe, Naphtali ^b   Andrian, Jean ^a   Adjouadi, Malek ^a  

^a The Department of Electrical and Computer Engineering   (United States)

^b FLORIDA INTERNATIONAL UNIVERSITY   (United States)

Author keywords

Face recognition; Multilinear principal component analysis; Nearest neighbor classifier; Support vector machine

Indexed keywords

CLASSIFICATION METHODS; DATA SETS; NEAREST NEIGHBOR CLASSIFIER; RECOGNITION ACCURACY;

ALGORITHMS; CLASSIFIERS; COMPUTATIONAL EFFICIENCY; FACE RECOGNITION; SUPPORT VECTOR MACHINES;

PRINCIPAL COMPONENT ANALYSIS;

ACCURACY; ALGORITHM; ARTICLE; CALCULATION; CONTROLLED STUDY; DATA BASE; FACE; FACIAL EXPRESSION; INFORMATION PROCESSING; INTERMETHOD COMPARISON; MULTILINEAR PRINCIPAL COMPONENT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; RECOGNITION; SUPPORT VECTOR MACHINE;

EID: 77952548066     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2009.08.022     Document Type: Article

References (31)

1. Zhao W., Chellappa R., Phillips P.J., Rosenfeld A. Face recognition: a literature survey. ACM Computing Surveys (CSUR) 2003, 35(4):399-458.
2. Turk M., Pentland A. Eigenfaces for recognition. Journal of Cognitive Neuroscience 1991, 3(1):71-86.
3. Bartlett M.S., Movellan JavierR., Sejnowski T.J. Face recognition by independent component analysis. IEEE Transactions on Neural Networks 2002, 13(6).
4. Kim K., Jung K., Kim H.J. Face recognition using kernel principal component analysis. IEEE Signal Processing Letters 2002, 9(2).
5. Yang J., Zhang D., et al. Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26(1):131-137.
6. H. Yu, M. Bennamoun, 1D-PCA, 2D-PCA to nD-PCA, in: The 18th International Conference on Pattern Recognition, 2006.
7. M. Visani, C. Garcia, C. Laurent, Comparing robustness of two-dimensional PCA and eigenfaces for face recognition, in: ICIAR 2004, Lecture Notes in Computer Science, vol. 3212, 2004, pp. 717-724.
8. A. Xu, X. Jin, Y. Jiang, P. Guo, Complete two-dimensional PCA for face recognition, in: The 18th International Conference on Pattern Recognition, 2006.
9. C. Lu, W. Liu, X. Liu, S. An, Double sides 2DPCA for face recognition, in: Lecture Notes in Computer Science, vol. 5226, Proceedings of the 4th International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications-with Aspects of Theoretical and Methodological Issues, 2008, pp. 446-459.
10. Kim Y., Song Y., Chang U., Kim D., Yun T., Ahn J. Face recognition using a fusion method based on bidirectional 2dpca. Applied Mathematics and Computation 2008, 205(2):601-607.
11. Kong H., Wang L., Teoh E.K., Li X., Wang J., Venkateswarlu R. Generalized 2d pca for face image representation and recognition. Neural Networks 2005, 18(5-6):585-594. (Special issue: IJCNN 2005).
12. Zhang D., Zhou Z. (2d)2 PCA: two-directional two-dimensional PCA for efficient face representation and recognition. Neurocomputing 2005, 69(1-3):224-231.
13. Kim C., Choi C. Image covariance-based subspace method for face recognition. Pattern Recognition 2007, 40(5):1592-1604.
14. V. Nhat, S. Lee, New feature extraction approaches for face recognition, in: Lecture Notes in Computer Science, vol. 3809/2005, pp. 489-497.
15. N. Nguyen, W. Liu, S. Venkates, Random subspace two-dimensional PCA for face recognition, in: Lecture Notes in Computer Science, vol. 4810, Advances in Multimedia Information Processing-PCM 2007, December 2007, pp. 655-664.
16. P. Sanguansat, W. Asdornwised, S. Marukatat, S. Jitapunkul, Two-dimensional random subspace analysis for face recognition, in: International Symposium on Communications and Information Technologies, 2007, 17-19 October 2007, pp. 628-631.
17. Xu Y., Zhang D., Yang J., Yang J. An approach for directly extracting features from matrix data and its application in face recognition. Neurocomputing 2008, 71(10-12):1857-1865.
18. Pan X., Ruan Q. PaImprint recognition using Gabor feature-based (2D)(2)PCA. Neurocomputing August 2008, 71(13-15):3032-3036.
19. Yu W., Wang Z., Chen W. A new framework to combine vertical and horizontal information for face recognition. Neurocomputing 2009, 72(4-6):1084-1091.
20. H. Lu, K.N. Plataniotis, A.N. Venetsanopoulos, multilinear principal component analysis of tensor objects for recognition, in: The 18th International Conference on Pattern Recognition, 2006.
21. Lu H., Plataniotis K.N., Venetsanopoulos A.N. MPCA: multilinear principal component analysis of tensor objects. IEEE Transactions on Neural Networks 2008, 19(1).
22. Tao D., Li X., Wu X., Maybank S. Tensor rank one discriminant analysis-a convergent method for discriminative multilinear subspace selection. Neurocomputing 2008, 71(10-12):1866-1882.
23. Tao D., Li X., Wu X., Maybank S. Geometry mean for subspace selection. IEEE Transaction on Pattern Analysis and Machine Intelligence 2009, 31(2):260-274.
24. Pang Y., Yuan Y., Li X. Effective feature extraction in high dimensional space. IEEE Transaction on Systems, Man and Cybernetics-Part B 2008, 38(6):1652-1656. B.C.
25. J. Wang, Y. Chen, M. Adjouadi, A comparative study of multilinear principal component analysis for face recognition, AIPR, in: 2008 37th IEEE Applied Imagery Pattern Recognition Workshop, 2008, pp. 1-6.
26. Ho T.K. The random subspace method for constructing decision trees. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998, 20(8):832-844.
27. N.V. Chawla, K. Bowyer, Random subspaces and subsampling for 2-D face recognition, in: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2005, pp. 582-589.
28. AT&T The database of faces, Online available. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.
29. Face Recognition Data, University of Essex, UK. Online available. http://cswww.essex.ac.uk/mv/allfaces/index.html.
30. Georgia Tech face database, Online available. http://www.anefian.com/facereco.htm.
31. C.C. Chang, C.J. Lin, LIBSVM: A Library for Support Vector Machines, 2001. Software available at. http://www.csie.ntu.edu.tw/~cjlin/libsvm.