Robust Verification With Subsurface Fingerprint Recognition Using Full Field Optical Coherence Tomography

Kiran B. Raja, Egidijus Auksorius, R. Raghavendra, A. Claude Boccara, Christoph Busch; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 144-152

Fingerprint recognition has been extensively used in number of civilian applications ranging from border control to everyday identity verification. The threats to current systems emerge from two facts that can be attributed to loss in accuracy due to damaged external fingerprint and attacks on the sensors by creation of the artefacts simply by lifting the latent fingerprints. In the growing necessity for secure biometric systems that are based on fingerprinting, new generation sensors have been investigated which can image the subsurface fingerprint. In this work, we explore the subsurface fingerprint imaging 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 fingerprints image 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 fingerprints and deduce the set of important conclusions for path forward in FF-OCT subsurface fingerprint imaging.