Enhanced SVD Based Face Recognition
|Published in:||Issue 1, (Vol. 6) / 2012|
|Author(s):||SHARIF Muhammad , ANIS Saad , RAZA Mudassar, MOHSIN Sajjad|
|Abstract.||One of the demanding tasks in face recognition is to handle illumination and expression variations. A lot of research is in progress to overcome such problems. This paper addresses the preprocessing method that is composed of grouping SVD perturbation and DWT. The proposed technique also performs well under one picture per person scenarios. The resulting image of this method is fed in to the simple SVD algorithm for face recognition. This paper performs its accuracy test on ORL, Yale, PIE and AR databases and focuses on the illumination problems|
|Keywords:||Eigen Face, Singular Value Decomposition (SVD), Discrete Wavelet Transforms (DWT), SVD Perturbation|
1. D.Q. Zhang, S.C. Chen, Z.-H. Zhou, “A new face recognition method based on SVD perturbation for single example image per person”, Appl. Math. Comp. 163 (2) (2005) 895–907.
2. R. Bmnelli, and T. Poggio, “Face Recognition: Features versus Templates”, IEEE Trans., Oct. 1993
3. L. Wiskott, J.-M. Fellous, N. Kruger, and C. von der Malsburg. “Face recognition by elastic bunch graph matching. IEEE Tans. on Pattern Analysis and Machine Intelligence 19 (7) (1997)775-779”
4. M. Turk and Pentland, “Eigenfaces for recognition”,Journal of cognitive neuro Science, March 1991
5. Chou-Hao Hsu and Chaur-Chin Chen,“SVD-Based Projection for Face Recognition”, IEEE EIT 2007 Proceedings
6. W. Zhao,r. Chellappa,P.J.Phillips and A. Rosenfeld, ”Face Recognition: A Literature Survey” ACM Computing Surveys, Vol. 35, No. 4, December 2003, pp. 399–458.
7. Xiaoyang Tana,b, Songcan Chena,c,¬, Zhi-Hua Zhoub, Fuyan Zhangb, “Face recognition from a single image per person: A survey ”, Pattern Recognition 39 (2006) 1725 – 1745
8. J. Yang and D. Zhang, A.F. Frangi, and J.Y.Yang, ”Twodimensional PCA: a new approach to appearance-based face representation and recognition”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp.131- 137, 2004.
9. J. Wu, Z.-H. Zhou, “Face recognition with one training image per person”, Pattern Recognition Lett. 23 (14) (2002) 1711– 1719.
10. S.C. Chen, D.Q. Zhang, Z.-H. Zhou, Enhanced (PC)2 A for face recognition with one training image per person, Pattern Recognition Lett. 25 (10) (2004) 1173–1181.
11. A. Yilmaz, M. Gohen, “Eigenhill vs. eigenface and eigenedge”, Pattern Recognition, vol. 34, pp. 181-184, 2001
12. Iulian B. Ciocoiu, Brenf Valmar, “a comparison between two preprocessing techniques in. pca-based face recognition”.
13. Ming yu, Gang yan, Qing-wen zhu, “New face recognition method based on dwt/dct combined feature selection” Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 13-16 August 2006
14. vcY. Z. Goh, Andrew B. J. Teoh, Michael K. O. Goh, “Wavelet Based Illumination Invariant Preprocessing in Face Recognition”, 2008 Congress on Image and Signal Processing.
16. A.U. Batur and M.H. Hayes, “Linear Subspace for Illumination.Robust Face Recognition,” Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition, Dec. 2001.
17. P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.
18. M. Belkin and P. Niyogi, “Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering,” Proc. Conf. Advances in Neural Information Processing System 15, 2001.
19. M. Belkin and P. Niyogi, “Using Manifold Structure for Partially Labeled Classification,” Proc. Conf. Advances in Neural Information Processing System 15, 2002
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