3D Face Recognition under Expressions, Occlusions, and Pose Variations
3D Face Recognition under Expressions, Occlusions, and Pose Variations
复制标题
DOI：
10.1109/tpami.2013.48
复制
发表时间：
2013-09-01
影响因子：
23.6
通讯作者：
Slama, Rim
中科院分区：
文献类型：
--
作者：
Drira, Hassen;Ben Amor, Boulbaba;Slama, Rim
我们提出了一种新的几何框架，用于分析3D人脸，其具体目标是比较，匹配和平均其形状。在这里，我们表示面部表面的放射状曲线从鼻尖发出，并使用这些曲线的弹性形状分析，以开发一个黎曼框架，用于分析形状的完整的面部表面。这种表示，沿着弹性黎曼度量，似乎自然测量面部变形，是强大的挑战，如大的面部表情（特别是那些张开嘴），大的姿势变化，丢失的部分，部分遮挡由于眼镜，头发，等。在实证评估方面，我们的结果匹配或改进了三个著名数据库的最新方法：FRGCv2，GavabDB和Bosphorus，每个数据库都提出了不同类型的挑战。从理论的角度来看，这个框架允许正式的统计推断，例如使用PCA在切线空间上估计缺失的面部部分和计算平均形状。
We propose a novel geometric framework for analyzing 3D faces, with the specific goals of comparing, matching, and averaging their shapes. Here we represent facial surfaces by radial curves emanating from the nose tips and use elastic shape analysis of these curves to develop a Riemannian framework for analyzing shapes of full facial surfaces. This representation, along with the elastic Riemannian metric, seems natural for measuring facial deformations and is robust to challenges such as large facial expressions (especially those with open mouths), large pose variations, missing parts, and partial occlusions due to glasses, hair, and so on. This framework is shown to be promising from both-empirical and theoretical-perspectives. In terms of the empirical evaluation, our results match or improve upon the state-of-the-art methods on three prominent databases: FRGCv2, GavabDB, and Bosphorus, each posing a different type of challenge. From a theoretical perspective, this framework allows for formal statistical inferences, such as the estimation of missing facial parts using PCA on tangent spaces and computing average shapes.