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.
Original language | English |
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Title of host publication | Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005 |
Pages | 73-76 |
Number of pages | 4 |
Volume | 2005 |
Publication status | Published - 1 Dec 2005 |
Event | 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005 - Hong Kong, Hong Kong Duration: 13 Dec 2005 → 16 Dec 2005 |
Conference
Conference | 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005 |
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Country/Territory | Hong Kong |
City | Hong Kong |
Period | 13/12/05 → 16/12/05 |
ASJC Scopus subject areas
- General Engineering