대한전자공학회:학술대회논문집 (Proceedings of the IEEK Conference)
- 대한전자공학회 2000년도 ITC-CSCC -2
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- Pages.873-876
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- 2000
PCA vs. ICA for Face Recognition
- Lee, Oyoung (Department of Electrical Eingineering, Chungbuk National University) ;
- Park, Hyeyoung (Department of Computer Science Yonsei University) ;
- Park, Seung-Jin (Department of Electrical Eingineering, Chungbuk National University)
- 발행 : 2000.07.01
초록
The information-theoretic approach to face recognition is based on the compact coding where face images are decomposed into a small set of basis images. Most popular method for the compact coding may be the principal component analysis (PCA) which eigenface methods are based on. PCA based methods exploit only second-order statistical structure of the data, so higher- order statistical dependencies among pixels are not considered. Independent component analysis (ICA) is a signal processing technique whose goal is to express a set of random variables as linear combinations of statistically independent component variables. ICA exploits high-order statistical structure of the data that contains important information. In this paper we employ the ICA for the efficient feature extraction from face images and show that ICA outperforms the PCA in the task of face recognition. Experimental results using a simple nearest classifier and multi layer perceptron (MLP) are presented to illustrate the performance of the proposed method.