基于稀疏模型的人脸姿态估计

Face Pose Estimation Based on Sparse Model

针对现有的人脸姿态估计方法易受"自遮挡"影响,采用改进的ASM算法提取人脸特征点,并利用人脸形态的几何统计知识来估计人脸特征点的深度值。以人脸主要特征点建立人脸稀疏模型,在利用相关人脸特征点近似估计人脸姿态后,通过最小二乘法精确估计三维人脸空间姿态。实验结果表明,对于"自遮挡"情况,该方法仍有较好的估计结果,与同类方法比较具有良好的姿态估计精度。

The method of face pose estimation is vulnerable to ＇self-occlusion＇ at present. To solve this problem, an improved ASM algorithm is used to extract facial feature points, and the geometric statistical knowledge of the face shape is used to estimate the depth of the facial feature points. Then the sparse face model is established based on the main features of human face. After estimating the face pose approximately with relevant face feature points, 3D space face pose is estimated accurately via the algorithm of least-squares method. The experiment results show that the method has better estimated results for the case of ＇self-occlusion＇, and has better estimation accuracy compared with the same kind of method.