Saliency model-based face segmentation and tracking in head-and-shoulder video sequences

In this paper, a novel face segmentation algorithm is proposed based on facial saliency map (FSM) for head-and-shoulder type video application. This method consists of three stages. The first stage is to generate the saliency map of input video image by our proposed facial attention model. In the second stage, a geometric model and an eye-map built from chrominance components are employed to localize the face region according to the saliency map. The third stage involves the adaptive boundary correction and the final face contour extraction. Based on the segmented result, an effective boundary saliency map (BSM) is then constructed, and applied for the tracking based segmentation of the successive frames. Experimental evaluation on test sequences shows that the proposed method is capable of segmenting the face area quite effectively.