When Facial Recognition Systems become Presentation Attack Detectors

Abstract

Recently, biometric systems (BSs) have experienced a broad development mainly due to the great success of deep learning approaches. Generally, most BS provide high security and efficiency. However, they are still vulnerable to attack presentations (APs). To overcome such security issues, these schemes include a Presentation Attack Detection (PAD) module which determines whether the input sample stems from an AP or a bona fide presentation (BP). Traditionally, most PAD subsystems assess the biometric sample prior to the recognition module. In this work, we evaluate to what extent the inverted combination, where the biometric recognition module filters samples prior to the assessment of a PAD mechanism, leads to an overall PAD performance improvement. The experimental evaluation conducted over two well-known databases including challenging attacks, reports a significant improvement in the detection performance when input samples were first filtered by the biometric recognition: only 1% of the APs are accepted while at most 5% BPs are rejected by the PAD subsystem.