姿态和表情变化下的三维人脸标志点定位
3D facial landmark localization under pose and expression variations

三维人脸标志点定位在人脸识别、人脸跟踪、人脸建模、表情分析等方面具有非常重要的作用. 然而, 在姿态和表情变化很大的条件下进行标志点定位, 这仍然是一个很具挑战性的课题. 本文提出一种对姿态和表情不敏感的三维人脸标志点定位方法, 利用HK曲率分析检测出候选标志点, 根据对面部形状的先验知识, 提出一种基于人脸几何结构的分类策略对候选标志点进一步细分, 通过把候选标志点与人脸标志点模型进行匹配, 实现标志点的精确定位. 首先在CASIA数据集对该方法的标志点定位精度进行测试, 然后在UND/FRGC v2.0数据集对该方法与其他方法进行比较. 实验结果表明该方法在姿态和表情变化很大的条件下具有高精度和高鲁棒性.
Landmark localization on 3D facial scans is important for face recognition, tracking, modeling, expression analysis, and so on. However, landmark localization in the presence of large pose and expression variations is still a great challenge. In this paper, a method for 3D facial landmark localization is presented. The method is insensitive to pose and expression. Candidate landmarks are detected using HK curvature analysis. According to the priori knowledge on facial shape, a facial geometrical structure-based classification strategy is proposed to subdivide the candidate landmarks. Landmark localization is obtained by matching candidate landmarks with a facial landmark model (FLM). The landmark localization accuracy of our method is first experimented on the CASIA dataset. Then, our method is compared with the state-of-the-art methods on the UND/FRGC v2.0 dataset. Experimental results confirm that our method achieves high accuracy and robustness both to large pose and expression variations.