Abstract
This paper presents a face recognition method using improved Weber local descriptor (IWLD) and improved Weber binary coding method. Compared to the existing Weber local descriptor, the proposed IWLD represent local patterns more effectively and accurately by introducing novel Weber magnitude and orientation components. In order to extract more discriminative and robust feature for face recognition, the IWBC is proposed to encode the cues embedded in IWLD. Moreover, to reduce the dimension of feature extracted by IWBC and enhance its discriminative ability, the block-based Fishers linear discriminant (BFLD) is employed to learn a projection matrix from the training set. Experimental results on three (AR, FERET and PolyU-NIR) challenging databases demonstrate the effectiveness and robustness of our proposed method.
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