论文标题
Defraudnet：End2END指纹欺骗检测贴剂级别的注意
DeFraudNet:End2End Fingerprint Spoof Detection using Patch Level Attention
论文作者
论文摘要
近年来，指纹识别系统在生物识别安全领域取得了显着进步，因为它在个人，国家和全球安全中起着重要作用。尽管所有这些显着的进步，但指纹识别技术仍然容易受到欺骗攻击的影响，这可能会严重危害用户安全性。跨传感器和交叉材料欺骗检测仍然每天出现无数的欺骗材料构成挑战，损害了传感器的互操作性和鲁棒性。本文提出了一种使用全球和局部指纹特征描述符的指纹欺骗检测的新方法。这些描述符是使用densenet提取的，该材料可显着改善跨传感器，跨材料和跨数据表的性能。一个新型的补丁注意网络用于查找最具歧视性的补丁以及网络融合。我们在四个公开可用数据集上评估了我们的方法：Livdet 2011、2013、2015和2017。进行了一系列全面的实验，以评估这些数据集的跨传感器，跨材料和跨数据库的性能。所提出的方法的平均准确度分别为Livdet 2017,2015和2011分别超过了Livdet 2015和2011的平均准确性99.16％和99.72％，分别超过了当前的最新结果3％和4％。
In recent years, fingerprint recognition systems have made remarkable advancements in the field of biometric security as it plays an important role in personal, national and global security. In spite of all these notable advancements, the fingerprint recognition technology is still susceptible to spoof attacks which can significantly jeopardize the user security. The cross sensor and cross material spoof detection still pose a challenge with a myriad of spoof materials emerging every day, compromising sensor interoperability and robustness. This paper proposes a novel method for fingerprint spoof detection using both global and local fingerprint feature descriptors. These descriptors are extracted using DenseNet which significantly improves cross-sensor, cross-material and cross-dataset performance. A novel patch attention network is used for finding the most discriminative patches and also for network fusion. We evaluate our method on four publicly available datasets:LivDet 2011, 2013, 2015 and 2017. A set of comprehensive experiments are carried out to evaluate cross-sensor, cross-material and cross-dataset performance over these datasets. The proposed approach achieves an average accuracy of 99.52%, 99.16% and 99.72% on LivDet 2017,2015 and 2011 respectively outperforming the current state-of-the-art results by 3% and 4% for LivDet 2015 and 2011 respectively.
