Compass local binary patterns for gender recognition of facial photographs and sketches

This paper presents a new feature extraction method, compass local binary pattern (CoLBP) for facial gender recognition. To achieve robustness, the proposed method first computes directional edge responses using eight Kirsch compass masks. Then, the spatial relationships among the neighboring pixels in each edge response are exploited independently with the help of local binary pattern (LBP) to enhance the discrimination capability. Finally, spatial histograms computed from these LBP images are concatenated to build a face descriptor. Our proposed descriptor efficiently extracts discriminating information from four different levels, including gradient, regional, global and directional level. The proposed method was evaluated on three datasets (color FERET, LFW, and Adience) containing facial photographs. In spite of a wide range of challenges (low resolution, variations in pose, expression, and illumination) present in the datasets, the proposed method provided promising classification performance in comparison with several existing benchmark methods, thereby validating its robustness. Moreover, this paper also investigates the gender recognition of facial sketches. The experiments carried out on two facial sketch datasets including CUFS and CUFSF also demonstrated better classification performance of the proposed method.