Abstract
Independent Component Analysis (ICA) is a popular approach for face recognition. However, face recognition is often a small sample size problem, which will weaken the recognition performance of ICA classifier. In this paper, a novel method is proposed to enhance ICA classifier for the small sample size problem. First, we use the random resampling method to generate some random independent subspaces, and a classifier is constructed in each subspace. Then a voting strategy is adopted to integrate these classifiers for discrimination. Experimental results on public available face database show that the proposed method can obvious improve the performance of ICA classifier.
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References
Sirovich, L., Kirby, M.: Low-dimensional Procedure for the Characterization of Human Faces. J. Opt. Soc. Amer., A 4(3), 519–524 (1987)
Turk, M.A., Pentland, A.P.: Eigenfaces for Recognition. J. Cognitive Neurosci. 3(1), 71–86 (1991)
Comon, P.: Independent Component Analysis – a New Concept? Signal Processing 36, 287–314 (1994)
Bartlett, M.S., Lades, H.M., Sejnowski, T.J.: Independent Component Representations for Face Recognition. In: Proc. of SPIE, vol. 2399, pp. 528–539 (1998)
Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: Proc. of 13th Int’l Conf. on Machine Learning, pp. 148–156 (1996)
Breiman, L.: Bagging Predictors. Machine Learning J. 24, 123–140 (1996)
Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. IEEE Trans. Pattern Analysis and Machine Intelligence 20, 832–844 (1998)
Bell, A.J., Sejnowski, T.J.: An Information-maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation 7, 1129–1159 (1995)
Philips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET Database and Evaluation Procedure for Face Recognition Algorithms. Image and Vision Computing 16, 295–306 (1998)
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© 2004 Springer-Verlag Berlin Heidelberg
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Cheng, J., Liu, Q., Lu, H., Chen, YW. (2004). Random Independent Subspace for Face Recognition. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_45
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DOI: https://doi.org/10.1007/978-3-540-30133-2_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23206-3
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