Face Recognition using Color and Edge Orientation Difference Histogram

Abstract

Face recognition is a challenging problem because of different illuminations, poses, facial expressions, and occlusions. In this paper, a new robust face recognition method is proposed based on color and edge orientation difference histogram. Firstly, color and edge orientation difference histogram is extracted using color, color difference, edge orientation and edge orientation difference of the face image. Then, backward feature selection is employed to reduce the number of features. Finally, Canberra measure is used to assess the similarity between the images. Color and edge orientation difference histogram shows uniform color difference and edge orientation difference between two neighboring pixels. This histogram will be effective for face recognition due to different skin colors and different edge orientations of the face image, which leads to different light reflection. The proposed method is evaluated on Yale and ORL face datasets. These datasets are consisted of gray-scale face images under different illuminations, poses, facial expressions and occlusions. The recognition rate over Yale and ORL datasets is achieved 100% and 98.75% respectively. Experimental results demonstrate that the proposed method outperforms the existing methods in face recognition.