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@INPROCEEDINGS{Mohammadi_InfoVAE_ICASSP_2020,
                      author = {Mohammadi, Amir and Bhattacharjee, Sushil and Marcel, S{\'{e}}bastien},
                    keywords = {cross-dataset evaluation, domain generalization, mobile biometrics, Presentation Attack Detection},
                    projects = {SWAN},
                       title = {IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS},
                   booktitle = {45th International Conference on Acoustics, Speech, and Signal Processing},
                        year = {2020},
                   publisher = {IEEE},
                         url = {https://gitlab.idiap.ch/bob/bob.paper.icassp2020_facepad_generalization_infovae},
                    abstract = {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.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2020/Mohammadi_InfoVAE_ICASSP_2020.pdf}
}