REPORT George_Idiap-RR-03-2022/IDIAP Robust Face Presentation Attack Detection with Multi-channel Neural Networks George, Anjith Marcel, Sébastien EXTERNAL http://publications.idiap.ch/attachments/reports/2021/George_Idiap-RR-03-2022.pdf PUBLIC Idiap-RR-03-2022 2022 Idiap March 2022 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.