AdaBoost Gabor Fisher Classifier for Face Recognition

This paper proposes the AdaBoost Gabor Fisher Classifier (AGFC) for robust face recognition, in which a chain AdaBoost learning method based on Bootstrap re-sampling is proposed and applied to face recognition with impressive recognition performance. Gabor features have been recognized as one of the most successful face representations, but it is too high dimensional for fast extraction and accurate classification. In AGFC, AdaBoost is exploited to select optimally the most informative Gabor features (hereinafter as AdaGabor features). The selected low-dimensional AdaGabor features are then classified by Fisher discriminant analysis for final face identification. Our experiments on two large-scale face databases, FERET and CAS-PEAL (with 5789 images of 1040 subjects), have shown that the proposed method can effectively reduce the dimensionality of Gabor features and greatly increase the recognition accuracy. In addition, our experimental results show its robustness to variations in facial expression and accessories.

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