Department of Informatics and Telecommunications, University of Athens, 17584 Ilisia, Greece.
IEEE Trans Pattern Anal Mach Intell. 2013 Jul;35(7):1552-64. doi: 10.1109/TPAMI.2012.247.
A 3D landmark detection method for 3D facial scans is presented and thoroughly evaluated. The main contribution of the presented method is the automatic and pose-invariant detection of landmarks on 3D facial scans under large yaw variations (that often result in missing facial data), and its robustness against large facial expressions. Three-dimensional information is exploited by using 3D local shape descriptors to extract candidate landmark points. The shape descriptors include the shape index, a continuous map of principal curvature values of a 3D object's surface, and spin images, local descriptors of the object's 3D point distribution. The candidate landmarks are identified and labeled by matching them with a Facial Landmark Model (FLM) of facial anatomical landmarks. The presented method is extensively evaluated against a variety of 3D facial databases and achieves state-of-the-art accuracy (4.5-6.3 mm mean landmark localization error), considerably outperforming previous methods, even when tested with the most challenging data.
提出并全面评估了一种用于 3D 面部扫描的 3D 地标检测方法。所提出方法的主要贡献是在大的偏航变化下（通常导致面部数据缺失）对 3D 面部扫描进行自动和姿态不变的地标检测，以及对大的面部表情的鲁棒性。通过使用 3D 局部形状描述符来提取候选地标点，利用三维信息。形状描述符包括形状指数、三维物体表面主曲率值的连续映射以及旋转图像，即物体三维点分布的局部描述符。通过将候选地标与面部解剖地标的 Facial Landmark Model（FLM）进行匹配来识别和标记候选地标。该方法在各种 3D 面部数据库中进行了广泛评估，达到了最先进的精度（4.5-6.3 毫米的平均地标定位误差），明显优于以前的方法，即使在最具挑战性的数据上进行测试也是如此。
