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When Facial Recognition Systems become Presentation Attack Detectors
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Datum
2022
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Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
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.