An improved approach for depth data based face pose estimation using particle swarm optimization

This paper presents an improved approach for face pose estimation based on depth data using particle swarm optimization (PSO). In this approach, the frontal face of the system-user is first initialized and its depth image is taken as a person-specific template. Each query face of that user is rotated and translated with respect to its centroid using PSO to match with the template. Since the centroid of each query face always changes with the face pose changing, a common reference point has to be defined to measure the exact transformation of the query face. Thus, the nose tips of the optimal transformed face and the query face are localized to recompute the transformation from the query face to the optimal transformed face that matched with the template. Using the recomputed rotation and translation information, finally, the pose of the query face can be approximated by the relative pose between the query face and the template face. Experiments on public database show that the accuracy of this new method is more than 99%, which is much higher than the best performance (< 91%) of existing work.