基于特征点的3D人脸姿态跟踪

3D face pose tracking based on feature matching

针对视频序列中的人脸跟踪问题,提出一种单摄像头的人脸3D姿态跟踪方法。利用SIFT特征点匹配算法来得到可靠的帧间特征匹配。将前帧与所选的关键帧特征匹配信息及融入到对当前的姿态估计中,利用SIFT特征点匹配算法来得到可靠的帧间特征匹配。最后通过利用RANSAC随机选取特征点对,并用POSIT和最小化误差组合的3D投影方法以迭代的方式得到精确的当前帧人脸姿态估计。通过多组实验数据对比,表明了该算法在严重遮挡、头部摆动幅度较大、匹配点较少的复杂情况干扰下仍具有鲁棒性,并且解决了3D人脸跟踪的漂移问题,实现对目标人脸的稳定跟踪,对比以往2D跟踪算法在复杂环境下具有明显的改善。

Aiming at the problem of object face tracking in the video sequence,an approach was proposed for 3D pose tracking with a single camera.SIFT （scale-invariant feature transform）algorithm is also used,which can get reliable inter-frame feature match.And the feature correspondence information from either previous frame or some selected key-frame is fused into the current frame pose estimation.The pose estimation is a classical problem which can be formulated as that of minimizing an error metric based on collinearity in object.Finally,the current face pose estimation is obtained via 3 D project from combination RANSAC and POSIT algorithm.Various set of experi-mental data shows the improvement of our algorithm over existing 2D matching algorithms especially in solving the pose drifting question when tracking agile motion,severe occlusion,drastic illumination change.