CONF Mohammadi_InfoVAE_ICASSP_2020/IDIAP IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS Mohammadi, Amir Bhattacharjee, Sushil Marcel, Sébastien cross-dataset evaluation domain generalization mobile biometrics Presentation Attack Detection EXTERNAL http://publications.idiap.ch/attachments/papers/2020/Mohammadi_InfoVAE_ICASSP_2020.pdf PUBLIC 45th International Conference on Acoustics, Speech, and Signal Processing 2020 IEEE https://gitlab.idiap.ch/bob/bob.paper.icassp2020_facepad_generalization_infovae URL Presentation 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.