A local spectral feature based face recognition approach for the one-sample-per-person problem

Face recognition for the one-sample-per-person problem has received increasing attention owing to its wide range of potential applications. However, since only one training image is available for each person, and the face images may have large appearance variations, how to achieve a high recognition accuracy is still a challenging work. 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. Since a good diversity is observed for the outputs of the weaker classifiers, a strategy of classifier committee learning is adopted to combine the results obtained from different local spectral features. Moreover, inspired by the fact that the iterations are completely independent of each other, a scheme of multiple worker based parallel computing is designed to improve the loop speed by distributing iterations to the MATLAB workers simultaneously. Experimental results on the standard databases demonstrate the feasibility and effectiveness of the proposed method.