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Face Recognition Using an Enhanced Independent Component Analysis Approach
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- Authors
- Keun-Chang Kwak, Witold Pedrycz
- Issue Date
- 2007-03
- Citation
- IEEE Transactions on Neural Networks, v.18, no.2, pp.530-541
- ISSN
- 1045-9227
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- This paper is concerned with an enhanced independent component analysis (ICA) and its application to face recognition. Typically, face representations obtained by ICA involve unsupervised learning and high-order statistics. In this paper, we develop an enhancement of the generic ICA by augmenting this method by the Fisher linear discriminant analysis (LDA); hence, its abbreviation, FICA. The FICA is systematically developed and presented along with its underlying architecture. A comparative analysis explores four distance metrics, as well as classification with support vector machines (SVMs). We demonstrate that the FICA approach leads to the formation of well-separated classes in low-dimension subspace and is endowed with a great deal of insensitivity to large variation in illumination and facial expression. The comprehensive experiments are completed for the facial-recognition technology (FERET) face database; a comparative analysis demonstrates that FICA comes with improved classification rates when compared with some other conventional approaches such as eigenface, fisherface, and the ICA itself. © 2007 IEEE.
- KSP Keywords
- Comparative analysis, Distance metric, Enhanced Independent Component Analysis(EICA), Face Database, Fisher linear discriminant analysis(FLDA), High-Order Statistics, Linear Discriminant Analysis(LDA), Low-dimension, Support VectorMachine(SVM), Well-separated, face recognition