Adaptive Mean Shift Based Face Tracking by Coupled Support Map

Abstract

The mean-shift algorithm is a local search technique that uses the similarity of the color distributed information between the target model and the local candidate image in the target. It has been proven to be superior in simplicity and stability of the technique and has been widely used for face tracking applications. However, one of the major problems in face tracking using color distribution is its vulnerability to backgrounds with similar color distribution, occlusion, and illumination changes. In this paper, we suggest a Coupled Support Map (CSM) to resolve the problem, and show the effectiveness of the robust adaptive mean-shift (AMS) face tracking method. Through series of experiments, we conclude the robustness of suggested algorithms against face size and sudden lighting changes.