An Effective Shape-Texture Weighted Algorithm for Multi-view Face Tracking in Videos

In this paper, an effective face tracking algorithm based on the combination of shape and texture information is proposed. The edge map is used to represent the shape of a face, while the texture information is characterized by the local binary pattern (LBP). As the face patterns to be tracked in consecutive frames are highly correlated, an accurate tracking can be achieved by searching for the shortest weighted feature distance between the face pattern and the possible face candidates. The weights of the shape and texture can be adapted for real-time tracking. Both the edge map and the LBP can, to a certain extent, alleviate the illumination effect. Moreover, skin-color-like objects will not be falsely tracked as a face. Our proposed algorithm complements the AdaBoost face detection algorithm to form a multi-view face-tracking system. Experimental results show that our algorithm can track faces in varying poses (tilted or rotated) in real time.