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3-D Face Recognition Using Geodesic-Map Representation and Statistical Shape Modelling

机译：使用测地图表示和统计形状建模的3-D人脸识别

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3-D face recognition research has received significant attention in the past two decades because of the rapid development in imaging technology and ever increasing security demand of modern society. One of its challenges is to cope with non-rigid deformation among faces, which is often caused by the changes of appearance and facial expression. Popular solutions to deal with this problem are to detect the deformable parts of the face and exclude them, or to represent a face in terms of sparse signature points, curves or patterns that are invariant to deformation. Such approaches, however, may lead to loss of information which is important for classification. In this paper, we propose a new geodesic-map representation with statistical shape modelling for handling the non-rigid deformation challenge in face recognition. The proposed representation captures all geometrical information from the entire 3-D face and provides a compact and expression-free map that preserves intrinsic geometrical information. As a result, the search for dense points correspondence in the face recognition task can be speeded up by using a simple image-based method instead of time-consuming, recursive closest distance search in 3-D space. An experimental investigation was conducted on 3-D face scans using publicly available databases and compared with the benchmark approaches. The experimental results demonstrate that the proposed scheme provides a highly competitive new solution for 3-D face recognition.
机译：由于成像技术的快速发展以及现代社会的安全性需求，过去二十年来，3-D人脸识别研究得到了重大关注。其中一个挑战是应对面部之间的非刚性变形，这通常是由外观和面部表情的变化引起的。处理此问题的流行解决方案是检测面部的可变形部分并排除它们，或者以稀疏签名点，曲线或不变性变形的模式表示面部。然而，这种方法可能导致丢失对分类重要的信息。在本文中，我们提出了一种具有统计形状建模的新的测地图表示，用于处理面部识别中的非刚性变形挑战。所提出的表示从整个3-D面捕获所有几何信息，并提供一种紧凑且无表达的地图，以保留内在的几何信息。结果，可以通过使用简单的基于图像的方法而不是耗时，递归最近的距离搜索在3-D空间中来加速对面部识别任务中的密集点对应的搜索。使用公开的数据库对3D脸部扫描进行了实验调查，并与基准方法进行比较。实验结果表明，该方案为3-D人脸识别提供了高度竞争的新解决方案。

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