Automatic fingerprint recognition systems suffer from the threat of presentation attacks due to their wide range of deployment in areas including national borders and commercial applications. A presentation attack can be performed by creating a spoof of a user's fingerprint with or without their consent. This paper presents a dynamic ensemble of deep CNN and handcrafted features to detect presentation attacks in known-material and unknown-material protocols of the livness detection competition. The proposed presentation attack detection model, in this way, utilizes the capabilities of both deep CNN and handcrafted features techniques and exhibits better performance than their individual performances. We have validated our proposed method on benchmark databases from the Liveness Detection Competition in 2015, 2017, and 2019, yielding overall accuracy of 96.10\%, 96.49\%, and 94.99\% on them, respectively. The proposed method outperforms state-of-the-art methods in terms of classification accuracy.

翻译：自动指纹识别系统因其在边境口岸和商业应用等领域的广泛部署，面临着呈现攻击的威胁。呈现攻击可通过在用户知情或不知情的情况下伪造其指纹来实施。本文提出了一种动态集成深度CNN与手工特征的方法，用于活体检测竞赛中已知材质与未知材质协议下的呈现攻击检测。所提出的呈现攻击检测模型以此方式同时利用了深度CNN与手工特征技术的能力，并展现出优于各自单独使用的性能。我们在2015、2017和2019年活体检测竞赛的基准数据库上验证了所提方法，分别取得了96.10%、96.49%和94.99%的整体准确率。该方法在分类准确率方面优于现有最先进方法。