부산대학교 도서관

Fingerprint recognition has been extensively used in numerous civilian applications ranging from border control to everyday identity verification. The threats to current systems emerge from two facts that can be attributed to potential loss in accuracy due to damaged external fingerprints and attacks on the sensors by creation of an artefacts (e.g. silicone finger) simply by lifting the latent fingerprints. In the growing need for attack resistant biometric fingerprint recognition that can be operated without supervision, a new generation of sensors has been investigated, which can capture the subsurface fingerprint pattern. In this work, we explore a subsurface fingerprint imaging technique by employing a custom-built in-house Full-Field Optical Coherent Tomography (FF-OCT) sensor for capturing the subsurface fingerprint. Further, we evaluate a newly constructed database of 200 unique fingerprint samples collected in 2 different sessions with 6 layers of fingerprint images corresponding to 6 subsurface fingerprints. We also propose a framework based on quality metrics to fuse the subsurface fingerprint images to achieve a robust verification accuracy, which has resulted in Equal Error Rate (EER) of 0%. We also provide an extensive set of experiments to gauge the reliability of subsurface fingerprint recognition and deduce a set of important conclusions for the path forward in FFOCT subsurface fingerprint imaging.