Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/197
Title: Is ICA significantly better than PCA for face recognition?
Authors: Yang, J
Zhang, DD 
Yang, JY
Keywords: Face recognition
Principal component analysis
Independent component analysis
ICA architecture
Issue Date: 2005
Publisher: IEEE
Source: Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV’05) How to cite?
Abstract: The standard PCA was always used as baseline algorithm to evaluate ICA-based face recognition systems in the previous research. In this paper, we examine the two architectures of ICA for image representation and find that ICA Architecture I involves a PCA process by vertically centering (PCA I), while ICA Architecture II involves a whitened PCA process by horizontally centering (PCA II). So, it is reasonable to use these two PCA versions as baseline algorithms to revaluate the ICA-based face recognition systems. The experiments were performed on the FERET face database. The experimental results show there is no significant performance differences between ICA Architecture I (II) and PCA I (II), although ICA Architecture II significantly outperforms the standard PCA. It can be concluded that the performance of ICA strongly depends on its involved PCA process. The pure ICA projection has little effect on the performance of face recognition.
URI: http://hdl.handle.net/10397/197
ISSN: 1550-5499
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