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A comparative study on handcrafted features v/s deep features for open-set fingerprint liveness detection
Pattern Recognition Letters ( IF 5.1 ) Pub Date : 2021-04-20 , DOI: 10.1016/j.patrec.2021.03.032
Shivang Agarwal , Ajita Rattani , C. Ravindranath Chowdary

A fingerprint liveness detector is a pattern classifier that is used to distinguish a live finger from a fake (spoof) one in the context of an automated fingerprint recognition system. As liveness detectors or presentation attack detectors are vulnerable to presentation attacks, the security and reliability of fingerprint recognition are compromised. Presentation attack detection mechanisms rely on handcrafted or deep features to classify an image as live or spoof. In addition, to strengthen the security, fingerprint liveness detectors should be robust to presentation attacks fabricated using unknown fabrication materials or fingerprint sensors. In this paper, we conduct a comprehensive study on the impact of handcrafted and deep features from fingerprint images on the classification error rate of the fingerprint liveness detection task. We use LBP, LPQ and BSIF as handcrafted features and VGG-19 and Residual CNN as deep feature extractors for this study. As the problem is targeted as an open-set problem, the emphasis is on achieving better robustness and generalization capability. In our observation, handcrafted features outperformed their deep counterparts in two of the three cases under the within-dataset environment. In the cross-sensor environment, deep features obtained a better accuracy, and in the cross-dataset environment, handcrafted features obtained a lower classification error rate.



中文翻译:

手工特征与深层特征进行开放式指纹活动度检测的比较研究

指纹活动度检测器是一种模式分类器,用于在自动指纹识别系统的情况下将活动手指与假(欺骗)手指区分开。由于活动检测器或演示攻击检测器容易受到演示攻击,因此指纹识别的安全性和可靠性受到损害。演示文稿攻击检测机制依靠手工制作或深度特征将图像分类为实时或欺骗。另外,为了增强安全性,指纹活动度检测器应对使用未知制造材料或指纹传感器制造的表示攻击具有鲁棒性。在本文中,我们对指纹图像的手工和深度特征对指纹活力检测任务的分类错误率的影响进行了全面研究。在本研究中,我们将LBP,LPQ和BSIF用作手工特征,并将VGG-19和残差CNN用作深度特征提取器。由于该问题的目标是开放集问题,因此重点在于获得更好的鲁棒性和泛化能力。在我们的观察中,在数据集内环境下,三种情况中的两种情况下,手工制作的功能胜过其深层的功能。在跨传感器环境中,深层特征获得更好的精度,而在跨数据集环境中,手工特征获得更低的分类错误率。在数据集内环境下,在三种情况中的两种情况下,手工制作的功能胜过其深层的功能。在跨传感器环境中,深层特征获得更好的精度,而在跨数据集环境中,手工特征获得更低的分类错误率。在数据集内环境下,在三种情况中的两种情况下,手工制作的功能胜过其深层的功能。在跨传感器环境中,深层特征获得更好的精度,而在跨数据集环境中,手工特征获得更低的分类错误率。

更新日期:2021-04-23
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