Robust 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 three-fold. 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. Experiment results obtained by using extensive dataset show that our system is effective and efficient in recognizing genders for uncontrolled environment of real life images.