Mohammadi, AmirBhattacharjee, SushilMarcel, Sébastien2020-03-182020-03-182020-03-18202010.1109/ICASSP40776.2020.9053922https://infoscience.epfl.ch/handle/20.500.14299/167403Presentation attack detection (PAD) is now considered critically important for any face-recognition (FR) based access-control system. Current deep-learning based PAD systems show excellent performance when they are tested in intra-dataset scenarios. Under cross-dataset evaluation the performance of these PAD systems drops significantly. This lack of generalization is attributed to domain-shift. Here, we propose a novel PAD method that leverages the large variability present in FR datasets to induce invariance to factors that cause domain-shift. Evaluation of the proposed method on several datasets, including datasets collected using mobile devices, shows performance improvements in cross-dataset evaluations.cross-dataset evaluationdomain generalizationmobile biometricsPresentation Attack Detectiontext::conference output::conference proceedings::conference paper