A novel class-dependence feature analysis method for face recognition

This paper develops a novel Class-dependence Feature Analysis (CFA) method for robust face recognition. A new Correlation filter called Optimal Origin Correlation output Tradeoff Filter (OOCTF) is designed in the two-dimensional (2-D) feature space obtained by Second-order Tensor Subspace An lysis (STSA). Designing correlation filters in the 2-D feature space makes them more tolerant to distortions in illumination and facial expression etc. Moreover, by focusing oil the Correlation Outputs at the origin, COCTF is very effective for feature vector extraction. Experimental results on three benchmark face databases show the superiority of the Proposed method over traditional face recognition methods. (c) 2008 Elsevier B.V. All rights reserved.