  Typical fingerprint recognition systems are comprised of a spoof detection
module and a subsequent recognition module, running one after the other. In
this paper, we reformulate the workings of a typical fingerprint recognition
system. In particular, we posit that both spoof detection and fingerprint
recognition are correlated tasks. Therefore, rather than performing the two
tasks separately, we propose a joint model for spoof detection and matching to
simultaneously perform both tasks 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 LiveDet
2015 dataset, both maintaining the performance of stand-alone 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. The project page for our work is available at
https://www.bit.ly/ijcb2021-unified .
