%Aigaion2 BibTeX export from Idiap Publications
%Thursday 21 November 2024 01:25:39 PM

@INCOLLECTION{George_SPRINGER_2023,
         author = {George, Anjith and Marcel, S{\'{e}}bastien},
       projects = {Idiap, ODIN/BATL},
          title = {Robust Face Presentation Attack Detection with Multi-channel Neural Networks},
      booktitle = {Handbook of Biometric Anti-Spoofing},
           year = {2023},
      publisher = {Springer},
       crossref = {George_Idiap-RR-03-2022},
            pdf = {https://publications.idiap.ch/attachments/papers/2023/George_SPRINGER_2023.pdf}
}



crossreferenced publications: 
@TECHREPORT{George_Idiap-RR-03-2022,
         author = {George, Anjith and Marcel, S{\'{e}}bastien},
       projects = {Idiap, ODIN/BATL},
          month = {3},
          title = {Robust Face Presentation Attack Detection with Multi-channel Neural Networks},
           type = {Idiap-RR},
         number = {Idiap-RR-03-2022},
           year = {2022},
    institution = {Idiap},
       abstract = {Vulnerability against presentation attacks remains a challenging issue
limiting the reliable use of face recognition systems. Though several methods have
been proposed in the literature for the detection of presentation attacks, the majority
of these methods fail in generalizing to unseen attacks and environments. Since the
quality of attack instruments keeps getting better, the difference between bonafide
and attack samples is diminishing making it harder to distinguish them using the
visible spectrum alone. In this context, multi-channel presentation attack detection
methods have been proposed as a solution to secure face recognition systems. Even
with multiple channels, special care needs to be taken to ensure that the model gener-
alizes well in challenging scenarios. In this chapter, we present three different strate-
gies to use multi-channel information for presentation attack detection. Specifically,
we present different architecture choices for fusion, along with ad-hoc loss func-
tions as opposed to standard classification objective. We conduct an extensive set
of experiments in the HQ-WMCA dataset, which contains a wide variety of attacks
and sensing channels together with challenging unseen attack evaluation protocols.
We make the protocol, source codes, and data publicly available to enable further
extensions of the work.},
            pdf = {https://publications.idiap.ch/attachments/reports/2021/George_Idiap-RR-03-2022.pdf}
}