Konferenzbeitrag
Fisher Vector Encoding of Dense-BSIF Features for Unknown Face Presentation Attack Detection
Vorschaubild nicht verfügbar
Volltext URI
Dokumententyp
Text/Conference Paper
Zusatzinformation
Datum
2020
Zeitschriftentitel
ISSN der Zeitschrift
Bandtitel
Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
The task of determining whether a sample stems from a real subject (i.e, it is a bona
fide presentation) or it comes from an artificial replica (i.e., it is an attack presentation) is a mandatory
requirement for biometric capture devices, which has received a lot of attention in the recent
past. Nowadays, most face Presentation Attack Detection (PAD) approaches have reported a good
detection performance when they are evaluated on known Presentation Attack Instruments (PAIs)
and acquisition conditions, in contrast to more challenging scenarios where unknown attacks are
included in the evaluation. For those more realistic scenarios, the existing approaches are in many
cases unable to detect unknown PAI species. In this work, we introduce a new feature space based
on Fisher vectors, computed from compact Binarised Statistical Image Features (BSIF) histograms,
which allows finding semantic feature subsets from known samples in order to enhance the detection
of unknown attacks. This new representation, evaluated over three freely available facial databases,
shows promising results in the top state-of-the-art: a BPCER100 under 17% together with a AUC
over 98% can be achieved in the presence of unknown attacks.