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.

翻译：基于接触的指纹生物识别是最流行的生物识别模态之一，广泛应用于多个领域。然而，接触式技术存在潜在指纹残留、多人接触同一表面导致的卫生问题等缺陷，促使学界探索非接触式解决方案。近年，非接触式指纹系统因能将任何带摄像头的设备转化为指纹读取器而备受关注且需求日增。但在充分利用非接触式方法优势之前，生物识别领域仍需解决若干问题，例如系统对呈现攻击的鲁棒性。当前主要障碍之一是公开数据集中伪造与活体数据的匮乏。本研究构建了一个包含7500余张四指图像、14000余张人工分割单指尖图像及10000个合成指尖（深度伪造）的呈现攻击检测（PAD）数据集。该数据集依据FIDO协议，从三种难度等级的六种不同呈现攻击仪器（PAI）中采集，涵盖五种PAI材料类型及不同智能手机摄像头的手动对焦模式。我们采用DenseNet-121与NasNetMobile模型，结合所构建数据集开发PAD算法，实现了呈现攻击分类错误率（APCER）0.14%与真实呈现分类错误率（BPCER）0.18%的检测精度。同时，我们还报告了模型对未见过的伪造类型测试结果，以模拟不确定的真实场景测试。