Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31704
Title: Kernel PCA with doubly nonlinear mapping for face recognition
Authors: Xie, XD
Lam, KM 
Keywords: Face recognition
Polynomials
Principal component analysis
Wavelet transforms
Issue Date: 2005
Publisher: IEEE
Source: Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems, 2005 : ISPACS 2005, 13-16 December 2005, p. 73-76 How to cite?
Abstract: In this paper, a novel Gabor-based kernel principal component analysis (PCA) with doubly nonlinear mapping is proposed for human face recognition. In our approach, the Gabor wavelets are used to extract facial features, then a doubly nonlinear mapping kernel PCA is devised to perform feature transformation and face recognition. Our algorithm is evaluated based on the Yale database, the AR database, the ORL database and the YaleB database by using different face recognition methods such as PCA, Gabor wavelets plus PCA, and Gabor wavelets plus kernel PCA with fractional power polynomial (FPP) models. Experiments show that consistent and promising results are obtained.
URI: http://hdl.handle.net/10397/31704
ISBN: 0-7803-9266-3
DOI: 10.1109/ISPACS.2005.1595349
Appears in Collections:Conference Paper

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