Real-time Gender Recognition for Uncontrolled Environment of Real-life Images

Gender recognition is a challenging task in real life images and surveillance videos due to their relatively low-resolution, under uncontrolled environment and variant viewing angles of human subject. Therefore, in this paper, a system of real-time gender recognition for real life images is proposed. The contribution of this work is fourfold. A skin-color filter is first developed to filter out non-face noises. In order to make the system robust, a mechanism of decision making based on the combination of surrounding face detection, context-regions enhancement and confidence-based weighting assignment is designed. Experimental results obtained by using extensive dataset show that our system is effective and efficient in recognizing genders for uncontrolled environment of real life images.