FDSNet:使用光场相机的手指背图像欺骗检测网络

Avantika Singh, Gaurav Jaswal, A. Nigam
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引用次数: 4

摘要

目前,生物识别系统容易受到伪造生物特征的欺骗攻击,对识别性能提出了很大的挑战。尽管存在广泛的表现攻击检测(PAD)或活体检测算法,但指纹传感器很容易受到假手指的欺骗。在这种情况下,手指背侧图像可以被认为是一种替代方案,可以在没有太多用户合作的情况下捕获,并且更适合户外安全应用。本文首次对指纹背侧认证系统的欺骗攻击场景进行了可行性研究,包括印刷纸、包装印刷纸、扫描和移动四种类型的表示攻击。本研究还提出了一种基于CNN的欺骗攻击检测方法,该方法采用了最先进的深度学习技术和迁移学习机制。我们收集了来自33名受试者的196张手指背的真实图像,用Lytro相机拍摄,并创建了一组784张手指背的欺骗图像。大量的实验结果表明,所提出的方法对各种欺骗攻击具有优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FDSNet: Finger dorsal image spoof detection network using light field camera
At present spoofing attacks via which biometric system is potentially vulnerable against a fake biometric characteristic, introduces a great challenge to recognition performance. Despite the availability of a broad range of presentation attack detection (PAD) or liveness detection algorithms, fingerprint sensors are vulnerable to spoofing via fake fingers. In such situations, finger dorsal images can be thought of as an alternative which can be captured without much user cooperation and are more appropriate for outdoor security applications. In this paper, we present a first feasibility study of spoofing attack scenarios on finger dorsal authentication system, which include four types of presentation attacks such as printed paper, wrapped printed paper, scan and mobile. This study also presents a CNN based spoofing attack detection method which employ state-of-the-art deep learning techniques along with transfer learning mechanism. We have collected 196 finger dorsal real images from 33 subjects, captured with a Lytro camera and also created a set of 784 finger dorsal spoofing images. Extensive experimental results have been performed that demonstrates the superiority of the proposed approach for various spoofing attacks.
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