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A quantitative comparison of 3D face databases for 3D face recognition

机译：3D面部识别3D面部数据库的定量比较

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摘要

During the last decade research in face recognition has shifted from 2D to 3D face representations. The need for 3D face data has resulted in the advent of 3D databases. In this paper, we first give an overview of publicly available 3D face databases containing expression variations, since these variations are an important challenge in today's research. The existence of many databases demands a quantitative comparison of these databases in order to compare more objectively the performances of the various methods available in literature. The ICP algorithm is used as baseline algorithm for this quantitative comparison for the identification and verification scenario, allowing to order the databases according to their inherent difficulty. Performance analysis using the rank 1 recognition rate for identification and the equal error rate for verification reveals that the FRGC v2 database can be considered as the most challenging. Therefore, we recommend to use this database further as reference database to evaluate (expression-invariant) 3D face recognition algorithms. As second contribution, the main factors that influence the performance of the baseline technique are determined and attempted to be quantified. It appears that (1) pose variations away from frontality degrade performance, (2) expression types affect results, (3) more intense expressions degrade recognition, (4) an increasing number of expressions decreases performance and (5) the number of gallery subjects degrades performace. A new 3D face recognition algorithm should be evaluated for all these factors.
机译：在过去十年中，人脸识别的研究已经从2D转移到3D面部表示。对3D面部数据的需求导致3D数据库的出现。在本文中，我们首先概述包含表达式变化的公开可用的3D面部数据库，因为这些变化是当今研究中的一个重要挑战。许多数据库的存在需要定量比较这些数据库，以便更客观地比较文献中可用的各种方法的性能。 ICP算法用作识别和验证场景的这种定量比较的基线算法，允许根据其固有难度订购数据库。使用秩1的识别识别率的性能分析和验证的相同错误率揭示了FRGC V2数据库可以被视为最具挑战性。因此，我们建议使用此数据库作为参考数据库来评估（表达式不变）3D面部识别算法。作为第二贡献，确定并试图量化影响基线技术性能的主要因素。看来（1）姿势变化远离正面降低性能，（2）表达类型影响结果，（3）更强烈的表达式降低识别，（4）越来越多的表达方式减少了性能和（5）播放器降级性能。应为所有这些因素评估新的3D面部识别算法。

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