Fingerprint Presentation Attack Detection With Fixed Prototype Contrastive Learning and Anomaly Detection

Fingerprint Presentation Attack Detection (PAD) is crucial for ensuring the reliability of fingerprint recognition systems. Mainstream methods typically frame PAD as a binary classification task. Although they achieve commendable results, these approaches often fail on unseen spoofs. This is because their decision boundaries rely heavily on trainset spoofs, leading to poor generalization. To address this issue, we reformulate PAD as an anomaly detection problem and propose the Fixed-Prototype PAD method. First, we introduce Fixed-Prototype Contrastive Learning (FPCL) for extracting PAD features. FPCL clusters the features of live fingerprints near a fixed prototype while pushing the features of spoof fingerprints away. We then design a Fixed-Prototype Anomaly Detection method to replace binary classification for decision-making. Specifically, this involves comparing the distance between test fingerprint features and the fixed live prototype to detect spoofs. Our method eliminates the dependency on the trainset spoofs during decision-making, thereby enhancing generalization capability. Experimental results demonstrate that our approach achieves state-of-the-art performance on the LivDet 2019 and LivDet 2021 benchmarks.