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Deep convolutional neural networks for face and iris presentation attack detection: survey and case study
IET Biometrics ( IF 2 ) Pub Date : 2020-08-25 , DOI: 10.1049/iet-bmt.2020.0004
Yomna Safaa El‐Din 1 , Mohamed N. Moustafa 2 , Hani Mahdi 1
Affiliation  

Biometric presentation attack detection (PAD) is gaining increasing attention. Users of mobile devices find it more convenient to unlock their smart applications with finger, face, or iris recognition instead of passwords. In this study, the authors survey the approaches presented in the recent literature to detect face and iris presentation attacks. Specifically, they investigate the effectiveness of fine-tuning very deep convolutional neural networks to the task of face and iris antispoofing. They compare two different fine-tuning approaches on six publicly available benchmark datasets. Results show the effectiveness of these deep models in learning discriminative features that can tell apart real from fake biometric images with a very low error rate. Cross-dataset evaluation on face PAD showed better generalisation than state-of-the-art. They also performed cross-dataset testing on iris PAD datasets in terms of equal error rate, which was not reported in the literature before. Additionally, they propose the use of a single deep network trained to detect both face and iris attacks. They have not noticed accuracy degradation compared to networks trained for only one biometric separately. Finally, they analysed the learned features by the network, in correlation with the image frequency components, to justify its prediction decision.

中文翻译:

用于人脸和虹膜表现攻击检测的深度卷积神经网络:调查和案例研究

生物特征表示攻击检测(PAD)越来越受到关注。移动设备的用户发现使用手指,面部或虹膜识别而不是密码来解锁智能应用程序更加方便。在这项研究中,作者调查了最近文献中介绍的检测面部和虹膜表现发作的方法。具体来说,他们研究了将非常深的卷积神经网络微调到面部和虹膜反欺骗任务的有效性。他们在六个可公开获得的基准数据集上比较了两种不同的微调方法。结果表明,这些深度模型在学习区分特征方面的有效性,这些特征可以将真实的生物特征图像与伪造的生物特征图像区分开,错误率极低。人脸PAD上的跨数据集评估显示出比最新技术更好的概括性。他们还以相等的错误率对虹膜PAD数据集进行了跨数据集测试,这在以前的文献中没有报道过。此外,他们建议使用经过训练可检测面部和虹膜攻击的单个深度网络。与仅针对一个生物特征进行单独训练的网络相比,他们没有注意到准确性下降。最后,他们分析了网络与图像频率成分相关的学习特征,以证明其预测决策是正确的。
更新日期:2020-08-28
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