Browse > Article

Face Recognition Robust to Local Distortion using Modified ICA Basis Images  

Kim Jong-Sun (연세대학교 생체인식연구센터)
Yi June-Ho (성균관대학교 정보통신공학부)
Publication Information
Journal of KIISE:Software and Applications / v.33, no.5, 2006 , pp. 481-488 More about this Journal
Abstract
The performance of face recognition methods using subspace projection is directly related to the characteristics of their basis images, especially in the cases of local distortion or partial occlusion. In order for a subspace projection method to be robust to local distortion and partial occlusion, the basis images generated by the method should exhibit a part-based local representation. We propose an effective part-based local representation method named locally salient ICA (LS-ICA) method for face recognition that is robust to local distortion and partial occlusion. The LS-ICA method only employs locally salient information from important facial parts in order to maximize the benefit of applying the idea of 'recognition by parts.' It creates part-based local basis images by imposing additional localization constraint in the process of computing ICA architecture I basis images. We have contrasted the LS-ICA method with other part-based representations such as LNMF (Localized Non-negative Matrix Factorization) and LFA (Local Feature Analysis). Experimental results show that the LS-ICA method performs better than PCA, ICA architecture I, ICA architectureII, LFA, and LNMF methods, especially in the cases of partial occlusions and local distortions.
Keywords
face recognition; part based local representation; ICA; LS-ICA;
Citations & Related Records
연도 인용수 순위
  • Reference
1 P. Belhumeur, J. Hespanha, and D. Kriegman, 'Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,' IEEE PAMI, vol. 19, no. 7, pp. 711-720, 1999   DOI   ScienceOn
2 A. Hyvarinen, 'The Fixed-point Algorithm and Maximum Likelihood Estimation for Independent Component Analysis,' Neural Processing Letters, vol. 10, pp. 1-5, 1999   DOI
3 Aapo Hyvarinen and Erki Oja, 'Independent component analysis: a tutorial,' http://www.cis.hut.fi/-aapo/papers/IJCNN99_tutorialweb/, 1999
4 J. S. Kim, J. M. Choi, J. H. Yi, 'ICA Based Face Recognition Robust to Partial Occlusion and Local Distortions,' ICBA 2004, LNCS, vol. 3072, pp. 147-154, 2004
5 M. A. Turk and A. P. Pentland, 'Eigenfaces for recognition,' Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991   DOI   ScienceOn
6 M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, 'Face Recognition by Independent Component Analysis,' IEEE Trans. Neural Networks, vol. 13, no. 6, pp. 1450-1464, 2002   DOI   ScienceOn
7 S. Wild, J. Curry, A. Dougherty, 'Motivating Non-Negative Matrix Factorizations,' In Proceedings of the Eighth SIAM Conference on Applied Linear Algebra, July 2003
8 B. A. Draper, K. Baek, M. S. Bartlett, and J. R. Beveridge, 'Recognizing faces with PCA and ICA,' Computer Vision and Image Understanding, vol. 91, no. 1, pp. 115-137, 2003   DOI   ScienceOn
9 M. S. Bartlett, Face Image Analysis by Unsupervised Learning, Kluwer Academic Publishers, 2001
10 http://www.uk.research.att.com/facedatabase.html
11 A. P. Pentland, 'Recognition by Parts,' IEEE Proceedings of the First International Conference on Computer Vision, pp. 612-620, 1987
12 P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, 'The FERET Evaluation Methodology for Face Recognition Algorithms,' IEEE PAMI, vol. 22, no. 10, pp. 1090-1104, 2000   DOI   ScienceOn
13 A. M. Martinez and R. Benavente, 'The AR face database,' CVC Tech, 1998
14 D. D. Lee and H. S. Seung, 'Learning the parts of objects by non-negative matrix factorization,' Nature, vol. 401, pp. 788-791, 1999   DOI   ScienceOn
15 S. Z. Li, X. W. Hou, H. J. Zhang, 'Learning Spatially Localized, Parts-Based Representation,' Computer Vision and Pattern Recognition, vol. 1, pp, 207-212, 2001   DOI
16 P. Penev and J. Atick, 'Local Feature Analysis: A general statistical theory for object representation,' Network: Computation in Neural Systems, vol. 7, no. 3, pp. 477-500, 1996   DOI   ScienceOn
17 A. J. Bell and T. J. Sejnowski, 'The Independent Components of Natural Scenes are Edge Filters,' Vision Research, vol. 37, no. 23, pp. 3327-3338, 1997   DOI   ScienceOn