Feature correlation filter for face recognition

Authors:
Xiangxin Zhu;Shengcai Liao;Zhen Lei;Rong Liu;Stan Z. Li
Affiliations:
Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Venue:
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Year:
2007

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Abstract

The correlation filters for pattern recognition, have been extensively studied in the areas of automatic target recognition(ATR) and biometrics. Whereas the conventional correlation filters perform directly on image pixels, in this paper, we propose a novel method, called "feature correlation filter (FCF)", by extending the concept of correlation filter to feature spaces. The FCF preserves the benefits of conventional correlation filters, i.e., shift-invariant, occlusion-insensitive, and closed-form solution, and also inherits virtues of the feature representations. Moreover, since the size of feature is often much smaller than the size of image, the FCF method can significantly reduce the storage requirement in recognition system. The comparative results on CMU-PIE and the FRGC2.0 database show that the proposed FCFs can achieve noteworthy performance improvement compared with their conventional counterpart.