Towards 3D Face Recognition in the Real: A Registration-Free Approach Using Fine-Grained Matching of 3D Keypoint Descriptors
Towards 3D Face Recognition in the Real: A Registration-Free Approach Using Fine-Grained Matching of 3D Keypoint Descriptors
复制标题
迈向现实中的 3D 人脸识别：使用 3D 关键点描述符细粒度匹配的免配准方法
DOI：
10.1007/s11263-014-0785-6
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发表时间：
2015-06-01
影响因子：
19.5
通讯作者：
Chen, Liming
中科院分区：
文献类型：
--
作者：
Li, Huibin;Huang, Di;Chen, Liming
在人脸扫描的点云或深度图像上执行的配准算法已经成功地用于表情变化下的自动3D人脸识别，但很少被研究来解决姿势变化和遮挡问题，这主要是因为初始化粗对齐的基本标志并不总是可用的。最近，基于局部特征的类SIFT匹配被证明能够在不注册的情况下处理所有这些变化。本文针对三维人脸识别在实际生物识别中的应用，将类SIFT匹配框架扩展到网格数据中，提出了一种基于三维关键点描述符的细粒度匹配方法。首先，提供了两个基于主曲率的3D关键点检测器，它们可以重复识别人脸扫描中局部曲率较高的互补位置。然后，在每个关键点建立一个稳健的3D局部坐标系，从而可以提取姿态不变的特征。设计了与三个表面微分量对应的三个关键点描述子，并利用它们的特征级融合来综合描述检测到的关键点的局部形状。最后，我们提出了一种基于多任务稀疏表示的细粒度匹配算法，该算法考虑了识别中由大型图库描述符稀疏表示的探针面描述符的平均重建误差。我们的方法在Bsporus数据库上进行了测试，在整个数据库以及表情、姿势和遮挡子集上的排名第一的识别率分别为96.56、98.82、91.14和99.21%。据我们所知，这些是迄今为止在该数据库上报告的最好结果。此外，在FRGC v2.0数据库上的实验也显示了良好的泛化能力。
Registration algorithms performed on point clouds or range images of face scans have been successfully used for automatic 3D face recognition under expression variations, but have rarely been investigated to solve pose changes and occlusions mainly since that the basic landmarks to initialize coarse alignment are not always available. Recently, local feature-based SIFT-like matching proves competent to handle all such variations without registration. In this paper, towards 3D face recognition for real-life biometric applications, we significantly extend the SIFT-like matching framework to mesh data and propose a novel approach using fine-grained matching of 3D keypoint descriptors. First, two principal curvature-based 3D keypoint detectors are provided, which can repeatedly identify complementary locations on a face scan where local curvatures are high. Then, a robust 3D local coordinate system is built at each keypoint, which allows extraction of pose-invariant features. Three keypoint descriptors, corresponding to three surface differential quantities, are designed, and their feature-level fusion is employed to comprehensively describe local shapes of detected keypoints. Finally, we propose a multi-task sparse representation based fine-grained matching algorithm, which accounts for the average reconstruction error of probe face descriptors sparsely represented by a large dictionary of gallery descriptors in identification. Our approach is evaluated on the Bosphorus database and achieves rank-one recognition rates of 96.56, 98.82, 91.14, and 99.21 % on the entire database, and the expression, pose, and occlusion subsets, respectively. To the best of our knowledge, these are the best results reported so far on this database. Additionally, good generalization ability is also exhibited by the experiments on the FRGC v2.0 database.