A Unified Model for Fingerprint Authentication and Presentation Attack Detection

Typical fingerprint recognition systems are comprised of a spoof detection module and a subsequent recognition module, running one after the other. In this study, we reformulate the workings of a typical fingerprint recognition system. We show that both spoof detection and fingerprint recognition are correlated tasks, and therefore, rather than performing these two tasks separately, a joint model capable of performing both spoof detection and matching simultaneously can be used without compromising the accuracy of either task. We demonstrate the capability of our joint model to obtain an authentication accuracy (1:1 matching) of TAR = 100% @ FAR = 0.1% on the FVC 2006 DB2A dataset while achieving a spoof detection ACE of 1.44% on the LivDet 2015 dataset, both maintaining the performance of standalone methods. In practice, this reduces the time and memory requirements of the fingerprint recognition system by 50 and 40%, respectively; a significant advantage for recognition systems running on resource constrained devices and communication channels.