多信息融合的多姿态三维人脸面部五官标志点定位方法

摘要

针对三维人脸模型面部五官标志点定位对姿态变化非常敏感的问题,提出了一种基于多信息融合的多姿态三维人脸五官标志点定位方法.首先对二维人脸纹理图像采用仿射不变的Affine- SIFT方法进行特征点检测,再利用映射关系将其投影到三维空间,并采用局部邻域曲率变化最大规则和迭代约束优化相结合的方法对面部五官标志点进行精确定位.在FRGC2.0和自建NPU3D数据库的实验结果表明,文中方法无需对姿态和三维数据的格式进行预先估计和定义,算法复杂度低,同时对人脸模型的姿态有着较强的鲁棒性,与现有五官标志点定位方法相比,有着更高的定位精度.%This paper proposes a novel multi-pose 3D facial landmark localization method based on multi-model information. In the presented method, affine invariant Affine-SIFT was utilized to 2D face texture image for feature point detection, the detected points were then mapped into the corresponding 3D face model. For 3D facial surface, the local neighbor curvature maximum change and iterative constraint optimization were combined to complete the facial landmark localization. The proposed method does not need estimate and define the posture of the face model and the format of 3D face mesh, therefore is more suitable for practical application. Experimental results on FRGC2. 0 and NPU3D face database show the proposed method is robust to face pose change, and has higher localization accuracy compared with the existing methods.