Robust gender recognition for real-time surveillance system

Gender recognition is a challenging task in surveillance videos due to their relatively low-solution, uncontrolled environment and viewing angles of human subject. In this work, a surveillance system of real-time gender recognition is developed. The contribution of this work is four-fold. In order to make the system robust, a mechanism of decision making based on the combination of neighboring face detection, context-regions enhancement and confidence-based weighting assignment is designed. Considering the spatiotemporal consistency of the gender between consecutive faces in successive frames, the belief propagation is employed to model the temporal coherence. Experiment results obtained by using extensive dataset show that our system is effective and efficient in recognizing genders in real-time surveillance videos.