On the generalization of color texture-based face anti-spoofing
On the generalization of color texture-based face anti-spoofing
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DOI：
10.1016/j.imavis.2018.04.007
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发表时间：
2018-09-01
影响因子：
4.7
通讯作者：
Hadid, Abdenour
中科院分区：
文献类型：
--
作者：
Boulkenafet, Zinelabidine;Komulainen, Jukka;Hadid, Abdenour
尽管人脸欺骗问题受到了极大的关注，但我们仍然缺乏在实际人脸识别系统中鲁棒执行的广义呈现攻击检测（PAD）方法。现有的人脸反欺骗技术在同一数据库（即内部测试协议）上进行训练和评估时确实取得了令人印象深刻的结果。然而，跨数据库的实验表明，最先进的方法的性能急剧下降，因为它们无法科普新的攻击场景和其他在训练和开发阶段没有看到的操作条件。到目前为止，即使是流行的卷积神经网络（CNN）也未能获得用于面部反欺骗的良好概括特征。在这项工作中，我们探讨了不同的因素，如采集条件和呈现攻击工具（派）的变化，对基于颜色纹理的人脸反欺骗的推广的影响。我们对七种基于颜色纹理的方法进行了广泛的跨数据库评估，结果表明大多数方法无法推广到看不见的欺骗攻击场景。更重要的是，实验表明，一些面部颜色纹理表示比其他人更强大的特定帕尔斯。从这个观察，我们提出了一个面对PAD解决方案的攻击特定的对策，完全基于颜色纹理分析，并调查如何以及它在不同的条件下，显示和打印攻击的推广。在三个基准人脸反欺骗数据库上结合攻击特定检测器的方法的评估显示出对显示攻击的显着泛化能力，而打印攻击需要进一步关注。(C)2018 Elsevier B.V.版权所有。
Despite the significant attention given to the problem of face spoofing, we still lack generalized presentation attack detection (PAD) methods performing robustly in practical face recognition systems. The existing face anti-spoofing techniques have indeed achieved impressive results when trained and evaluated on the same database (i.e. intra-test protocols). Cross-database experiments have, however, revealed that the performance of the state-of-the-art methods drops drastically as they fail to cope with new attacks scenarios and other operating conditions that have not been seen during training and development phases. So far, even the popular convolutional neural networks (CNN) have failed to derive well-generalizing features for face anti-spoofing. In this work, we explore the effect of different factors, such as acquisition conditions and presentation attack instrument (PAI) variation, on the generalization of color texture-based face anti spoofing. Our extensive cross-database evaluation of seven color texture-based methods demonstrates that most of the methods are unable to generalize to unseen spoofing attack scenarios. More importantly, the experiments show that some facial color texture representations are more robust to particular PAls than others. From this observation, we propose a face PAD solution of attack-specific countermeasures based solely on color texture analysis and investigate how well it generalizes under display and print attacks in different conditions. The evaluation of the method combining attack-specific detectors on three benchmark face anti-spoofing databases showed remarkable generalization ability against display attacks while print attacks require still further attention. (C) 2018 Elsevier B.V. All rights reserved.