Using Spectral Feature for Face Recognition of One-Sample-Per-Person Problem

In this paper, we propose a more accurate local spectral feature based face recognition approach for the one-sample-per-person problem. In the proposed algorithm, multi-resolution local spectral features are first extracted to represent the face images to enlarge the training set. A weaker classifier is then constructed based on the spectral features of each local region. A strategy of classifier committee learning is proposed further to combine the results obtained from different local spectral features. Experimental results on the standard databases demonstrate the feasibility and effectiveness of the proposed method.