Feature Correlation Filter for Face Recognition

XiangXin Zhu, Shengcai Liao, Zhen Lei, Rong Liu, Stan Z. Li

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.