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Expression-Invariant 3D Face Recognition

机译:表情不变的3D人脸识别

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摘要

We present a novel 3D face recognition approach based on geometric invariants introduced by Elad and Kimmel. The key idea of the proposed algorithm is a representation of the facial surface, invariant to isometric deformations, such as those resulting from different expressions and postures of the face. The obtained geometric invariants allow mapping 2D facial texture images into special images that incorporate the 3D geometry of the face. These signature images are then decomposed into their principal components. The result is an efficient and accurate face recognition algorithm that is robust to facial expressions. We demonstrate the results of our method and compare it to existing 2D and 3D face recognition algorithms.
机译:我们提出了一种基于Elad和Kimmel提出的几何不变量的新颖3D人脸识别方法。所提出算法的关键思想是对等角形变形不变的面部表面表示,例如由于面部表情和姿势不同而产生的变形。所获得的几何不变量允许将2D面部纹理图像映射为包含面部3D几何形状的特殊图像。然后将这些签名图像分解为它们的主要成分。结果是对面部表情具有鲁棒性的高效且准确的面部识别算法。我们演示了该方法的结果,并将其与现有的2D和3D人脸识别算法进行了比较。

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