A New Approach in Automated Fingerprint Presentation Attack Detection Using Optical Coherence Tomography

Presentation attack detection (PAD) is a critical component of automated fingerprint recognition systems (AFRSs). However, existing PAD technologies based on optical coherence tomography (OCT) mainly rely on local information, ignoring the global continuity and correlation of physiological structures. Furthermore, the lack of appropriate presentation attack instruments (PAIs) that cater to the unique OCT characteristics leads to the insufficient evaluation of PAD. The identification features, including external fingerprint (EF), internal fingerprint (IF), and subcutaneous sweat pore (SSP), provide valuable information about the intrinsic connections of physiological structures. Such intrinsic connections hold potential clues for PAD. Building upon this premise, this paper proposed a novel PAD method based on three OCT hand-crafted features: EF-IF self-matching score (SMS), SSP number (SN), and SSP coincidence rate (SCR). These simple yet effective PAD features offer a more precise and detailed description of the internal physiological structure, enabling accurate distinction between presentation attack (PA) and bona-fide. The proposed method achieves a 4% Equal Error Rate (EER), significantly outperforming other existing PAD methods. Additionally, the cross-device experiment demonstrates the generalization capability of the proposed method on both our dataset and the public OCT dataset.