Robust face tracking using bilateral filtering

This paper introduces a method for face tracking in a video sequence in real time. In this method the profile of color distribution characterizes target's feature. It is invariant for rotation and scale changes. It's also robust to non-rigidity and partial occlusion of the target. We employ the mean-shift algorithm to track the target face and to reduce the computational cost. However, face tracking using color distribution is failed by noises as occlusion including some objects with similar color distribution and with exactly difference color distribution. Thus failures are critical problems. To solve these problems, we employ a bilateral filter which uses the color and range information. We have applied the proposed bilateral filter to track the real time face tracking. The experimental results demonstrate the efficiency of this algorithm. Its performance has been proven superior to the original mean shift tracking algorithm.