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Face Recognition Using IPCA-ICA Algorithm
June 2006 (vol. 28 no. 6)
pp. 996-1000
In this paper, a fast incremental principal non-Gaussian directions analysis algorithm, called IPCA-ICA, is introduced. This algorithm computes the principal components of a sequence of image vectors incrementally without estimating the covariance matrix (so covariance-free) and at the same time transforming these principal components to the independent directions that maximize the non-Gaussianity of the source. Two major techniques are used sequentially in a real-time fashion in order to obtain the most efficient and independent components that describe a whole set of human faces database. This procedure is done by merging the runs of two algorithms based on principal component analysis (PCA) and independent component analysis (ICA) running sequentially. This algorithm is applied to face recognition problem. Simulation results on different databases showed high average success rate of this algorithm compared to others.

[1] J. Karhunen and J. Joutsensalo, “Representation and Separation of Signals Using Non Linear PCA Type Learning,” Neural Networks, vol. 7, no. 1, 1994.
[2] P. Common, “Independent Component Analysis, a New Concept?” Signal Processing, vol. 36, no. 3, 1994.
[3] J.J. Atick and A.N. Redlich, “What Does the Retina Know about Natural Scenes?” Neural Computing, vol. 4, pp. 196-210, 1992.
[4] J.F. Cardoso, “Blind Signal Separation, Statistical Principles,” Proc. IEEE, vol. 9, no. 10, 1998.
[5] H. Murase and S.K. Nayar, “Visual Learning and Recognition of 3-D Objects from Appearance,” Int'l J. Computer Vision, vol. 14, no. 1, pp. 5-24, Jan. 1995.
[6] Y. Cui and J. Weng, “Appearance-Base Hand Sign Recognition from Intensity Image Sequences,” Computer Vision and Image Understanding, vol. 78, pp. 157-176, 2000.
[7] S. Chen and J. Weng, “State-Based SHOSLIF for Indoor Visual Navigation,” IEEE Trans. Neural Networks, vol. 11, no. 6, pp. 1300-1314, 2000.
[8] Proc. NSF/DARPA Workshop Development and Learning, J. Weng and I. Stockman, eds., Apr. 2000.
[9] E.P. Simoncelli, “Higher Order Statistical Models of Visual Images,” Proc. IEEE Workshop Higher Order Statistics, June 1999.
[10] M. Turk and A. Pentland, “Eigenfaces for Recognition,” J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
[11] “ORL Face Database,” AT&T Laboratories Cambridge, , 2005.
[12] “UMIST Face Database,” Daniel Graham, , 2005.
[13] “Yale Face Database,” Columbia Univ., imagesyalefaces/, 2005.
[14] Y. Wang, T. Tan, and Y. Zhu, “Face Verification Based on Singular Value Decomposition and Radial Basis Function Neural Network+,” Inst. Automation, Chinese Academy of Sciences, Beijing, P.R. China, 100080. 2000.
[15] H. Cevikalp, M. Neamtu, M. Wilkes, and A. Barkana, “Discriminative Common Vectors for Face Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 1, pp. 6-9, Jan. 2005.
[16] K. Fukunaga, Introduction to Statistical Pattern Recognition, second ed. New York: Academic Press, pp. 831-836, Aug. 1996.
[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] J. Rubner and K. Schulten, “Development of Feature Detectors by Self-Organization,” Biological Cybernetics, vol. 62, pp. 193-199, 1990.
[19] Principles of Neural Science, E.R. Kandel, J.H. Schwartz, and T.M. Jessell, eds., third ed. Norwalk, Conn.: Appleton and Lange, 1991.

Index Terms:
IPCA-ICA, Principal component analysis (PCA), independent component analysis (ICA), principal non-Gaussian directions, image processing, blind source separation.
Issam Dagher, Rabih Nachar, "Face Recognition Using IPCA-ICA Algorithm," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 6, pp. 996-1000, June 2006, doi:10.1109/TPAMI.2006.118
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