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12 April 2007 Correlation filters for large population face recognition
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Reliable person recognition is important for secure access and commercial applications requiring human identification. Face recognition (FR) is an important technology being developed for human identification. Algorithms and systems for large population face recognition (LPFR) are of significant interest in applications such as watch lists and video surveillance. In this paper, we present correlation filter-based feature analysis methods that effectively exploit available generic training data to represent a large number of subjects and thus improve the performance for LPFR. We first introduce a general framework - class-dependence feature analysis (CFA), which uses correlation filters to provide a discriminant feature representation for LPFR. We then introduce two variants of the correlation filter-based CFA methods: 1) the kernel correlation filter CFA (KCFA) that generates nonlinear decision boundaries and significantly improves the recognition performance without greatly increasing the computational load, and 2) the binary coding CFA that uses binary coding to reduce the number of correlation filters and applies error control coding (ECC) to improve the recognition performance. These two variants offer ways to tradeoff between the computational complexity and the recognition accuracy of the CFA methods. We test our proposed algorithms on the face recognition grand challenge (FRGC) database and show that the correlation filter-based CFA approach improves the recognition rate and reduces the computational load over the conventional correlation filters.
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B.V.K. Vijaya Kumar, Chunyan Xie, and Marios Savvides "Correlation filters for large population face recognition", Proc. SPIE 6539, Biometric Technology for Human Identification IV, 65390F (12 April 2007);

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