Regularized kernel discriminant analysis with a robust kernel for face recognition and verification

Zafeiriou, Stefanos and Tzimiropoulos, Georgios and Petrou, Maria and Stathaki, Tania (2012) Regularized kernel discriminant analysis with a robust kernel for face recognition and verification. IEEE Transactions on Neural Networks and Learning Systems, 23 (3). pp. 526-534. ISSN 2162-237X

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We propose a robust approach to discriminant kernel-based feature extraction for face recognition and verification. We show, for the first time, how to perform the eigen analysis of the within-class scatter matrix directly in the feature space. This eigen analysis provides the eigenspectrum of its range space and the corresponding eigenvectors as well as the eigenvectors spanning its null space. Based on our analysis, we propose a kernel discriminant analysis (KDA) which combines eigenspectrum regularization with a feature-level scheme (ER-KDA). Finally, we combine the proposed ER-KDA with a nonlinear robust kernel particularly suitable for face recognition/verification applications which require robustness against outliers caused by occlusions and illumination changes. We applied the proposed framework to several popular databases (Yale, AR, XM2VTS) and achieved state-of-the-art performance for most of our experiments.

Item Type: Article
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Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Computer Science
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Depositing User: Tzimiropoulos, Yorgos
Date Deposited: 01 Feb 2016 11:11
Last Modified: 17 Sep 2016 00:10

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