Abstract
In this paper, we introduce a new feature representation method for face recognition. The proposed method, referred as Kernel ICA, combines the strengths of the Kernel and Independent Component Analysis approaches. For performing Kernel ICA, we employ an algorithm developed by F. R. Bach and M. I. Jordan. This algorithm has proven successful for separating randomly mixed auditory signals, but it has never been applied on bidimensional signals such as images. We compare the performance of Kernel ICA with classical algorithms such as PCA and ICA within the context of appearance-based face recognition problem using the FERET database. Experimental results show that both Kernel ICA and ICA representations are superior to representations based on PCA for recognizing faces across days and changes in expressions.
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References
Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face Recognition: A literature survey. Technical Report CART-TR-948. University of Maryland (August 2002)
Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face Recognition by Independent Component Analysis. IEEE Transactions on Neural Networks 13(6) (November 2002)
Ruiz, A., de Teruel, P.E.L.: Nonlinear kernel-based statistical pattern analysis. IEEE Transactions on Neural Networks 12(1), 16–32 (2001)
Kim, K.I., Jung, K., Kim, H.J.: Face Recognition Using Kernel Principal Component Analysis. IEEE Signal Processing Letters 9(2) (February 2002)
Yang, M.H.: Kernel eigenfaces vs. Kernel fisherfaces: Face Recognition using kernel methods. In: Proc. 5th Int. Conf. Automat. Face Gesture Recognition, Washington, DC, May 2002, pp. 215–220 (2002)
Bach, F.R., Jordan, M.I.: Kernel Independent Component Analysis. J. Machine Learning Res. 3, 1–48 (2002)
Barlow, H.B.: Unsupervised learning Neural Comput., vol. 1, pp. 295–311 (1989)
Atick, J.J.: Could information theory provide an ecological theory of sensory processing? Network 3, 213–251 (1992)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Hyvärinen, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999)
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Martiriggiano, T., Leo, M., D’Orazio, T., Distante, A. (2005). Face Recognition by Kernel Independent Component Analysis. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_7
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DOI: https://doi.org/10.1007/11504894_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26551-1
Online ISBN: 978-3-540-31893-4
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