Abstract
Touch-based fingerprint biometrics is one of the most popular biometric modalities with applications in several fields. Problems associated with touch-based techniques such as the presence of latent fingerprints and hygiene issues due to many people touching the same surface motivated the community to look for non-contact-based solutions. For the last few years, contactless fingerprint systems are on the rise and in demand because of the ability to turn any device with a camera into a fingerprint reader. Yet, before we can fully utilize the benefit of noncontact-based methods, the biometric community needs to resolve a few concerns such as the resiliency of the system against presentation attacks. One of the major obstacles is the limited publicly available data sets with inadequate spoof and live data. In this publication, we have developed a Presentation attack detection (PAD) dataset of more than 7500 four-finger images and more than 14,000 manually segmented single-fingertip images, and 10,000 synthetic fingertips (deepfakes). The PAD dataset was collected from six different Presentation Attack Instruments (PAI) of three different difficulty levels according to FIDO protocols, with five different types of PAI materials, and different smartphone cameras with manual focusing. We have utilized DenseNet-121 and NasNetMobile models and our proposed dataset to develop PAD algorithms and achieved PAD accuracy of Attack presentation classification error rate (APCER) 0.14\% and Bonafide presentation classification error rate (BPCER) 0.18\%. We have also reported the test results of the models against unseen spoof types to replicate uncertain real-world testing scenarios.
Abstract (translated)
基于触摸的指纹识别是几种领域最受欢迎的指纹识别模式之一。与触摸技术相关的一些问题,例如潜在的指纹残留和由于许多人同时触摸同一表面而产生的卫生问题,促使了指纹识别社区寻找非接触式的解决方案。过去几年中,无接触式指纹识别系统正在增长并受到需求,因为它们可以将任何带有摄像头的设备变成指纹读取器。然而,在我们充分利用非接触式方法的好处之前,指纹识别社区需要解决一些关注点,例如系统对演示攻击的抵御能力。其中一个主要障碍是缺乏足够的假数据和实际数据。在本文中,我们开发了超过7,500张四指图像和超过14,000张手动分割的单指tip图像,以及10,000张合成 fingertips (Deepfakes)的演示攻击检测数据集(PAD)。该数据集从六个不同的演示攻击工具(PAI)按照FIDO协议从三个不同难度级别收集,使用了五种不同的PAI材料,以及不同手动聚焦的智能手机摄像头。我们使用DenseNet-121和 NasNetMobile模型及其提出的数据集来开发pad算法,并实现了攻击演示分类错误率(APCER)为0.14%和正当演示分类错误率(BPCER)为0.18%的pad精度。我们还报告了模型对未观察到的假类型的结果测试结果,以模拟不确定的现实世界测试场景。
URL
https://arxiv.org/abs/2303.05459
