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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© 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
Online ISBN: 978-3-540-30133-2
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