Kantarcı, AlperenDertli, HasanEkenel, Hazım KemalBrömme, ArslanBusch, ChristophDamer, NaserDantcheva, AntitzaGomez-Barrero, MartaRaja, KiranRathgeb, ChristianSequeira, AnaUhl, Andreas2021-10-042021-10-042021978-3-88579-709-8https://dl.gi.de/handle/20.500.12116/37473Face anti-spoofing is essential to prevent false facial verification by using a photo, video, mask, or a different substitute for an authorized person's face. Most of the state-of-the-art presentation attack detection (PAD) systems suffer from overfitting, where they achieve near-perfect scores on a single dataset but fail on a different dataset with more realistic data. This problem drives researchers to develop models that perform well under real-world conditions. This is an especially challenging problem for frame-based presentation attack detection systems that use convolutional neural networks (CNN). To this end, we propose a new PAD approach, which combines pixel-wise binary supervision with patch-based CNN. We believe that training a CNN with face patches allows the model to distinguish spoofs without learning background or dataset-specific traces. We tested the proposed method both on the standard benchmark datasets ---Replay-Mobile, OULU-NPU--- and on a real-world dataset. The proposed approach shows its superiority on challenging experimental setups. Namely, it achieves higher performance on OULU-NPU protocol 3, 4 and on inter-dataset real-world experiments.enFace antispoofingPresentation attack detectionConvolutional neural networksReal-world datasetText/Conference Paper1617-5468