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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

Authors

Stefanos Zafeiriou

Georgios Tzimiropoulos

Maria Petrou

Tania Stathaki



Abstract

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.

Journal Article Type Article
Publication Date Mar 1, 2012
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
APA6 Citation 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), doi:10.1109/TNNLS.2011.2182058
DOI https://doi.org/10.1109/TNNLS.2011.2182058
Publisher URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6129513
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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