DepthPulse: A Passive Liveness Detection Framework for Face Presentation Attacks

Abstract

Face Presentation Attacks (FPA) are a growing concern for face authentication systems. In FPA, attackers use face representations of the authorized user and present them to the camera for authentication. FPA can be devised using different mediums such as printed photos (photo attacks), images or videos on a device (video attacks), or wearing a face mask (mask attacks). The mediums to implement these attacks are called Face Presentation Attack Instruments (F-PAI). There are numerous Face Presentation Attack Detection (F-PAD) methods, each individually designed to defend against most or all types of F-PAI. In this thesis, we first review a few existing F-PAD methods and perform a qualitative evaluation based on the published literature to create a taxonomic mapping of F-PAD to F-PAI. Then, we propose DepthPulse, an ensemble framework, that combines two F-PAD methods; depth estimation and remote photoplethysmography (rPPG) signal processing. Our contributions are three-fold: i) we identify preprocessing techniques that enhance depth-based liveness detection method; ii) we apply Discrete Fourier transformation methods to rPPG-based liveness detection, and iii) with DepthPulse, we reduced the ACER by 5% for Protocol 1, 14% for Protocol 2, 8% for Protocol 3, and 0.7% for Protocol 4 of the OULU-NPU dataset compared to the results from the state-of-the-art F-PAD method.