COLFIPAD: A Presentation Attack Detection Benchmark for Contactless Fingerprint Recognition

Contactless fingerprint recognition is an emerging biometric technology and Presentation Attack Detection (PAD) methods are crucial to preserve system security. Convolutional Neural Networks (CNNs) represent the state-of the-art of PAD algorithms for many contactless captured biometric characteristics and various research groups proposed specialized CNN-based PAD methods or used general purpose CNNs to detect Presentation Attacks (PAs). In this work, we compare nine CNN-based PAD methods for contactless fingerprint PAD: five general purpose algorithms, and four dedicated PAD methods designed for various biometric characteristics. To achieve this, we combine the COLFISPOOF database with three bona fide databases: the HDA database and both versions of the ISPFD database. We set up our experiments using a baseline evaluation protocol and four Leave-One-Out (LOO) protocols, to benchmark the generalization capabilities to unseen data. The results reported by using the Attack Presentation Classification Error Rate (APCER) vs. Bona fide Presentation Classification Error Rate (BPCER) and the Detection Equal Error Rate (D-EER). Further, we discuss the achieved results in detail and give recommendations for real-world implementations. Our results show that established PAD algorithms for other biometric characteristics can accurately detect PAs on contactless fingerprints. While strong deviations between the considered PAD algorithms are observed, the best performing method shows a D-EER between 0.01% and 0.08% (depending on the LOO partition) and a APCER of 0.00% at a BPCER of 1.00%.