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3D Face Recognition under Expressions, Occlusions, and Pose Variations

机译:表情,遮挡和姿势变化下的3D人脸识别

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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.
机译:我们提出了一种用于分析3D人脸的新颖几何框架,其特定目标是比较,匹配和平均其形状。在这里,我们通过鼻尖发出的径向曲线表示面部表面,并使用这些曲线的弹性形状分析来开发用于分析整个面部表面形状的黎曼框架。这种表示形式与弹性黎曼度量标准一样,对于测量面部变形似乎很自然,并且对于诸如面部表情较大(尤其是张口的面部表情),较大的姿势变化,缺少的部位以及由于眼镜,头发,等等。从经验和理论的角度来看,该框架都显示出了良好的前景。在实证评估方面,我们的结果与三个著名数据库FRGCv2,GavabDB和Bosphorus上的最新方法相匹配或有所改进,每个数据库都带来了不同类型的挑战。从理论上讲,此框架允许进行正式的统计推断,例如在切线空间上使用PCA估计缺少的面部部分并计算平均形状。

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