Twins 3D face recognition challenge

V. Vijayan, K. Bowyer, P. Flynn, Di Huang, Liming Chen, M. Hansen, Omar Ocegueda, S. Shah, I. Kakadiaris
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引用次数: 67

Abstract

Existing 3D face recognition algorithms have achieved high enough performances against public datasets like FRGC v2, that it is difficult to achieve further significant increases in recognition performance. However, the 3D TEC dataset is a more challenging dataset which consists of 3D scans of 107 pairs of twins that were acquired in a single session, with each subject having a scan of a neutral expression and a smiling expression. The combination of factors related to the facial similarity of identical twins and the variation in facial expression makes this a challenging dataset. We conduct experiments using state of the art face recognition algorithms and present the results. Our results indicate that 3D face recognition of identical twins in the presence of varying facial expressions is far from a solved problem, but that good performance is possible.
双胞胎3D人脸识别挑战
现有的3D人脸识别算法已经在像FRGC v2这样的公共数据集上取得了足够高的性能,很难进一步实现识别性能的显著提高。然而,3D TEC数据集是一个更具挑战性的数据集,它包括107对双胞胎的3D扫描,这些扫描是在一次会话中获得的,每个受试者都有一个中性表情和一个微笑表情的扫描。与同卵双胞胎面部相似性和面部表情变化相关的因素的组合使这成为一个具有挑战性的数据集。我们使用最先进的人脸识别算法进行实验并展示结果。我们的研究结果表明,在不同面部表情的情况下对同卵双胞胎进行3D人脸识别还远远没有解决问题,但良好的性能是可能的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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