Random Independent Subspace for Face Recognition
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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