Statistical non-rigid ICP algorithm and its application to 3D face alignment
Statistical non-rigid ICP algorithm and its application to 3D face alignment
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DOI：
10.1016/j.imavis.2016.10.007
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发表时间：
2017-02-01
影响因子：
4.7
通讯作者：
Pantic, Maja
中科院分区：
文献类型：
--
作者：
Cheng, Shiyang;Marras, Ioannis;Pantic, Maja
在过去的15-20年里，将3D面部模型与3D网格相匹配的问题受到了广泛的关注。大多数技术适用于由简单的可参数化面或平均3D面部形状组成的一般模型。这种方法的缺点是很难仅使用单个人脸模型来描述人脸的非刚性方面。捕捉3D面部变形的一种方法是通过面部或其部分的统计3D模型。当我们想要捕捉嘴部区域的变形时，这一点尤其明显。尽管人脸的统计模型通常被应用于面部强度的建模，但很少有方法适合3D人脸的统计模型。为了捕捉和描述人脸表面的非刚体特性，我们建立了一个基于局部的三维人脸表面统计模型，并将其与非刚体迭代最近点算法相结合。实验结果表明，该算法在三维人脸的匹配和对齐方面明显优于现有的算法，尤其是在嘴部区域的描述方面。(C)2016爱思唯尔B.V.保留所有权利。
The problem of fitting a 3D facial model to a 3D mesh has received a lot of attention the past 15-20years. The majority of the techniques fit a general model consisting of a simple parameterisable surface or a mean 3D facial shape. The drawback of this approach is that is rather difficult to describe the non-rigid aspect of the face using just a single facial model. One way to capture the 3D facial deformations is by means Of a statistical 3D model of the face or its parts. This is particularly evident when we want to capture the deformations of the mouth region. Even though statistical models of face are generally applied for modelling facial intensity, there are few approaches that fit a statistical model of 3D faces. In this paper, in order to capture and describe the non-rigid nature of facial surfaces we build a part-based statistical model of the 3D facial surface and we combine it with non-rigid iterative closest point algorithms. We show that the proposed algorithm largely outperforms state-of-the-art algorithms for 3D face fitting and alignment especially when it comes to the description of the mouth region. (C) 2016 Elsevier B.V. All rights reserved.