Fingerprint Presentation Attack Detection with Supervised Contrastive Learning.

The security of Automated Fingerprint Identification Systems (AFIS) heavily relies on the performance of the Fingerprint Presentation Attack Detection (FPAD) methods. However, the difficulty of FPAD lies in how to have strong robustness and generalization to unseen spoof fingerprints. To address this issue, we propose a novel FPAD framework with tailored Supervised Contrastive Learning (SupCon) and KNN-based OOD detection (KNN-OOD) method. We tailor the SupCon to better constrain the distribution of learned features by incorporating dynamic feature and label queues into SupCon and actively mining positive samples from the queues. In FPAD, we consider fingerprints with the same PAD label as intra-class, while those with different labels as inter-class. The tailored SupCon makes intra-class features more compact and inter-class features more dispersed. Utilizing the compact live fingerprint feature distribution, during the testing phase, we employ KNNOOD as an alternative to commonly used classification approaches. Since this approach does not rely on the distribution of trainset spoof fingerprints, it consistently achieves outstanding results even for unseen spoof fingerprints. Experiment results demonstrate that our proposed FPAD-SupCon framework achieves state-of-the-art performance on LivDet 2019 and LivDet 2021 datasets.