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

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

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.

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), https://doi.org/10.1109/TNNLS.2011.2182058

• 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
• DOI: https://doi.org/10.1109/TNNLS.2011.2182058
• Public URL: https://nottingham-repository.worktribe.com/output/1007943
• Publisher URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6129513
• 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