Kakadiaris Ioannis A, Passalis Georgios, Toderici George, Murtuza Mohammed N, Lu Yunliang, Karampatziakis Nikos, Theoharis Theoharis
Department of Computer Science, University of Houston, Houston, TX 77204, USA.
IEEE Trans Pattern Anal Mach Intell. 2007 Apr;29(4):640-9. doi: 10.1109/TPAMI.2007.1017.
In this paper, we present the computational tools and a hardware prototype for 3D face recognition. Full automation is provided through the use of advanced multistage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact metadata. We present our results on the largest known, and now publicly available, Face Recognition Grand Challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, this is the highest performance reported on the FRGC v2 database for the 3D modality.
在本文中，我们展示了用于3D人脸识别的计算工具和硬件原型。通过使用先进的多级对齐算法实现完全自动化，采用可变形模型框架来抵御面部表情，以及通过适当的预处理步骤实现对3D捕获设备的不变性。此外，通过将3D面部扫描转换为紧凑的元数据，在时间和空间上都实现了可扩展性。我们在已知最大且现已公开可用 的由数千次扫描组成的人脸识别大挑战3D面部数据库上展示了我们的结果。据我们所知，这是在FRGC v2数据库上针对3D模态所报告的最高性能。
