Multiple Fisher Classifiers Combination for Face Recognition based on Grouping AdaBoosted Gabor Features

Gabor features has been recognized as one of the most successful representation methods, such as Elastic Graph Matching, Gabor Fisher Classifier, and AdaBoost Gabor Fisher Classifier. One of the key issues in using Gabor features is how to efficiently reduce its high dimensionality. This paper proposes a multiple Fisher classifiers combination approach based on re-grouping Gabor features selected by using re-sampling and AdaBoost. At least two advantages can be observed with the proposed method: (1) more discriminative Gabor features are exploited in distributed Fisher classifiers by re-grouping the selected Gabor features; (2) combination of multiple Fisher classifiers improves the final performance of the classification compared with the traditional Fisher classifier. Our extensive experiments on two large face databases, FERET and CAS-PEAL, have impressively shown the effectiveness of the proposed method.