Accurate face localization in videos using effective information propagation
In this paper, we present a novel approach to accurate detection and tracking of human faces in videos. The idea is to propagate the information of a group of seed faces detected off-line with high certainties to recover the faces undetected or detected with only low confidence. Specifically, our approach first estimates from the color of the seed faces a person-specific skin color model, and based on which a particle filtering is performed for sequential face tracking. Then, a backward propagation scheme is devised to optimize the overall localization results in terms of smoothness. Experimental results demonstrate the efficacy of our approach.