Abstract
Face recognition is an important subject in computer vision and authentication systems. Feature extraction is one of the main steps in the face recognition systems, which greatly affects recognition accuracy. In the most of the existing methods, only local features in the facial area are extracted and employed in recognizing the person’s face. In this article, at first a novel multi-scale and rotation invariant global feature descriptor is introduced by applying the Zernike moment on the outputs of Gabor filters. Then the proposed global feature along with an efficient local feature, the histogram of oriented gradient (HOG), is employed to propose a new face recognition system. The proposed system was tested on three famous face recognition databases, namely ORL, Yale and AR and face recognition rates of 98%, 97.8% and 97.1% were obtained respectively. These rates are higher than other state-of-the-art methods.
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