This paper presents a simple and novel approach for robust face recognition. Extended multi-scale Weber local descriptor based on unfixed size patches is proposed to obtain micro and macro patterns. Then the recognition is performed using nearest neighborhood method based on Chi-square distance. Voting strategy is utilized in classification to reduce the impacts of the poor quality regions in face image caused by pose, illumination variations. Experiments on CMU-PIE, FERET and AR databases show that the proposed method has higher recognition accuracy in comparison with traditional methods.