A new Global-Gabor-Zernike feature descriptor and its application to face recognition

•This article combines structural and statistical features for face recognition.•Statistical features are introduced by applying the Zernike moments on Gabor filters.•The HOG descriptor is used to extract local statistical features.•The proposed method outperforms other methods on ORL, Yale, AR and LFW datasets.

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.