Face Recognition by Independent Component Analysis

In a task such as face recognition, much of the important information may be contained in the high-order relationships among the image pixels. Some success has been attained using data-driven face representations based on principal component analysis, such as "Eigenfaces" (Turk & Pentland, 1991) and "Holons" (Cottrell & Metcalfe, 1991). Principal component analysis (PCA) is based on the second-order statistics of the image set, and does not address high-order statistical dependencies such as the relationships among three or more pixels. Independent component analysis (ICA) is a generalization of PCA which separates the high-order moments of the input in addition to the second-order moments. We developed image representations based on the independent components of the face images and compared them to a PCA representation for face recognition.

ICA was performed on the face images under two different architectures. The first architecture provided a set of statistically independent basis images for the faces that can be viewed as a set of independent facial features. These ICA basis images were spatially local, unlike the PCA basis vectors. The representation consisted of the coefficients for the linear combination of basis images that comprised each face image. The second architecture produced independent coding variables (coefficients). This provided a factorial face code, in which the probability of any combination of features can be obtained from the product of their individual probabilities. The distributions of these coefficents were sparse and highly kurtotic. Classification was performed using nearest neighbor, with similarity measured as the cosine of the angle between representation vectors. Both ICA representations were superior to the PCA representation for recognizing faces across sessions, changes in expression, and changes in pose.

Long F, Wu T, Movellan J, Bartlett M, Littlewort, G (2012). Learning spatiotemporal features by using independent component analysis with application to facial expression recognition. Neurocomputing 93: 126-132 (2012). Download pdf

Bartlett, M.S., Movellan, J.R., & Sejnowski, T.J. (2002). Face recognition by independent component analysis. IEEE Transactions on Neural Networks 13(6) p. 1450-64.
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Bartlett, M. S., (2001). Face Image Analysis by Unsupervised Learning. Foreword by Terrence J. Sejnowski. Kluwer International Series on Engineering and Computer Science, V. 612. Boston: Kluwer Academic Publishers. Summary

Bartlett, M. Stewart (1998). Face image analysis by unsupervised learning and redundancy reduction. Doctoral dissertation, University of California, San Diego. 1.9 megs compressed. 
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Bartlett, M. Stewart, Lades, H. Martin, and Sejnowski, T.J. (1998). Independent component representations for face recognition. Proceedings of the SPIE, Vol 3299: Conference on Human Vision and Electronic Imaging III, p. 528-539.
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Bartlett, M. Stewart, and Sejnowski, T. J. (1997). Viewpoint invariant face recognition using independent component analysis and attractor networks. In M. Mozer, M. Jordan, T. Petsche (Eds.), Advances in Neural Information Processing Systems 9, MIT Press, Cambridge, MA. 817-823. 
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Facial expression analysis using ICA: Comparison to other methods

Bartlett, M.S., Donato, G.L., Movellan, J.R., Hager, J.C., Ekman, P., and Sejnowski, T.J. (2000). Image representations for facial expression coding. In S. Solla, T.Leen, & K. Mueller, Eds. Advances in Neural Information Processing Systems 12, Cambridge, MA: MIT Press, p. 886-892.
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Donato, G.L., Bartlett, M.S., Hager, J.C., Ekman, P., and Sejnowski, T.J. (1999). Classifying Facial Actions. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(10) p. 974-989.
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Matlab Code

Matlab code for face representations using ICA. Full code now available for architectures I and II. Updated 6/2005.