Multi-channel Face Presentation Attack Detection Using Deep Learning

Abstract

Face recognition has emerged as a widely used biometric modality. However, its vulnerability to presentation attacks remains a significant security threat. Although Presentation Attack Detection (PAD) methods attempt to remedy this problem, often they fail in generalizing to unseen attacks and environments. As the quality of presentation attack instruments improves over time, achieving reliable PA detection using only visual spectra remains a major challenge. We argue that multi-channel systems could help solve this problem. In this chapter, we first present an approach based on a multi-channel convolutional neural network for the detection of presentation attacks. We further extend this approach to a one-class classifier framework by introducing a novel loss function that forces the network to learn a compact embedding for the bonafide class while being far from the representation of attacks. The proposed framework introduces a novel way to learn a robust PAD system from bonafide and available (known) attack classes. The superior performance in unseen attack samples in publicly available multi-channel PAD database WMCA shows the effectiveness of the proposed approach. Software, data, and protocols for reproducing the results are made publicly available.