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

Zafeiriou, Stefanos; Tzimiropoulos, Georgios; Petrou, Maria; Stathaki, Tania


Stefanos Zafeiriou

Georgios Tzimiropoulos

Maria Petrou

Tania Stathaki


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.


Zafeiriou, S., Tzimiropoulos, G., Petrou, M., & Stathaki, T. (2012). Regularized kernel discriminant analysis with a robust kernel for face recognition and verification. IEEE Transactions on Neural Networks and Learning Systems, 23(3),

Journal Article Type Article
Publication Date Mar 1, 2012
Deposit Date Feb 1, 2016
Publicly Available Date Feb 1, 2016
Journal IEEE Transactions on Neural Networks and Learning Systems
Electronic ISSN 2162-237X
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 23
Issue 3
Public URL
Publisher URL
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