This Article 
 Bibliographic References 
 Add to: 
Effective Representation Using ICA for Face Recognition Robust to Local Distortion and Partial Occlusion
December 2005 (vol. 27 no. 12)
pp. 1977-1981
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 Nonnegative Matrix Factorization) and LFA (Local Feature Analysis). Experimental results show that the LS-ICA method performs better than PCA, ICA architecture I, ICA architecture II, LFA, and LNMF methods, especially in the cases of partial occlusions and local distortions.

[1] M.A. Turk and A.P. Pentland, “Eigenfaces for Recognition,” Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
[2] 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.
[3] A. Hyvarinen and E. Oja, “Independent Component Analysis: A Tutorial,” /, 1999.
[4] A. Hyvärinen, “The Fixed-Point Algorithm and Maximum Likelihood Estimation for Independent Component Analysis,” Neural Processing Letters, vol. 10, pp. 1-5, 1999.
[5] P. Belhumeur, J. Hespanha, and D. Kriegman, “Eigenfaces versus Fisherfaces: Recognition Using Class Specific Linear Projection,” IEEE Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, 1999.
[6] 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.
[7] 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.
[8] P.J. Phillips, H. Moon, S.A. Rizvi, and P.J. Rauss, “The FERET Evaluation Methodology for Face Recognition Algorithms,” IEEE Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1090-1104, 2000.
[9] A.M. Martinez and R. Benavente, “The AR Face Database,” CVC Tech, 1998.
[10] , 1994.
[11] A.P. Pentland, “Recognition by Parts,” IEEE Proc. First Int'l Conf. Computer Vision, pp. 612-620, 1987.
[12] D.D. Lee and H.S. Seung, ”Learning the Parts of Objects by Nonnegative Matrix Factorization,” Nature, vol. 401, pp. 788-791, 1999.
[13] 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.
[14] S.Z. Li, X.W. Hou, and H.J. Zhang, “Learning Spatially Localized, Parts-Based Representation,” Computer Vision and Pattern Recognition, vol. 1, pp. 207-212, 2001.
[15] S. Wild, J. Curry, and A. Dougherty, “Motivating Non-Negative Matrix Factorizations,” Proc. Eighth SIAM Conf. Applied Linear Algebra, July 2003.
[16] M.S. Bartlett, Face Image Analysis by Unsupervised Learning. Kluwer Academic, 2001.

Index Terms:
Index Terms- Face recognition, part-based local representation, ICA, LS-ICA.
Jongsun Kim, Jongmoo Choi, Juneho Yi, Matthew Turk, "Effective Representation Using ICA for Face Recognition Robust to Local Distortion and Partial Occlusion," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 12, pp. 1977-1981, Dec. 2005, doi:10.1109/TPAMI.2005.242
Usage of this product signifies your acceptance of the Terms of Use.