Random Independent Subspace for Face Recognition
- Cite this paper as:
- 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
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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