Deep patch-wise supervision for presentation attack detection

Face recognition systems have been widely deployed in various applications, such as online banking and mobile payment. However, these systems are vulnerable to face presentation attacks, which are created by people who obtain biometric data covertly from a person or through hacked systems. In order to detect these attacks, convolutional neural networks (CNN)-based systems have gained significant popularity recently. CNN-based systems perform very well on intra-data set experiments, yet they fail to generalise to the data sets that they have not been trained on. This indicates that they tend to memorise data set-specific spoof traces. To mitigate this problem, the authors propose a Deep Patch-wise Supervision Presentation Attack Detection (DPS-PAD) model approach that combines pixel-wise binary supervision with patch-based CNN. The authors’ experiments show that the proposed patch-based method forces the model not to memorise the background information or data set-specific traces. The authors extensively tested the proposed method on widely used PAD data sets—Replay-Mobile and OULU-NPU—and on a real-world data set that has been collected for real-world PAD use cases. The proposed approach is found to be superior on challenging experimental setups. Namely, it achieves higher performance on OULU-NPU protocol 3, 4 and on inter-data set real-world experiments.