Face Recognition Using Local and Global Features

The combining classifier approach has proved to be a proper way for improving recognition performance in the last two decades. This paper proposes to combine local and global facial features for face recognition. In particular, this paper addresses three issues in combining classifiers, namely, the normalization of the classifier output, selection of classifier(s) for recognition, and the weighting of each classifier. For the first issue, as the scales of each classifier's output are different, this paper proposes two methods, namely, linear-exponential normalization method and distribution-weighted Gaussian normalization method, in normalizing the outputs. Second, although combining different classifiers can improve the performance, we found that some classifiers are redundant and may even degrade the recognition performance. Along this direction, we develop a simple but effective algorithm for classifiers selection. Finally, the existing methods assume that each classifier is equally weighted. This paper suggests a weighted combination of classifiers based on Kittler's combining classifier framework. Four popular face recognition methods, namely, eigenface, spectroface, independent component analysis (ICA), and Gabor jet are selected for combination and three popular face databases, namely, Yale database, Olivetti Research Laboratory (ORL) database, and the FERET database, are selected for evaluation. The experimental results show that the proposed method has 5–7% accuracy improvement.