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Proceedings Paper

Face recognition and verification with pose and illumination variations and imposter rejection
Author(s): Chao Yuan; David P. Casasent
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Paper Abstract

We address rejection-classification problems, which have been ignored in most prior work. For such a system, a high classification rate and a low false alarm rate are simultaneously desired. We first propose a one-class support vector representation machine (SVRM). The SVRM achieves a high test set detection rate by requiring a high training set detection rate; the SVRM reduces the false alarm rate by minimizing the upper bound of the decision region. The SVRM is then extended to a new support vector representation and discrimination machine (SVRDM) classifier to address multiple-class cases. The theoretical basis for our new SVRDM as best at rejection of non-objects (imposters in face recognition) is provided, as are new σ parameter selection methods. Test results on face recognition and verification with both pose and illumination variations are presented.

Paper Details

Date Published: 28 March 2005
PDF: 14 pages
Proc. SPIE 5779, Biometric Technology for Human Identification II, (28 March 2005); doi: 10.1117/12.593419
Show Author Affiliations
Chao Yuan, Siemens Corporate Research (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 5779:
Biometric Technology for Human Identification II
Anil K. Jain; Nalini K. Ratha, Editor(s)

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