Fully supervised contrastive learning in latent space for face presentation attack detection

The vulnerability of conventional face recognition systems to face presentation or face spoofing attacks has attracted a great deal of attention from information security, forensic, and biometric communities during the past few years. With the recent advancement and availability of cutting-edge computing technologies, sophisticated and computationally expensive solutions to many problems have been made possible. Accordingly, deep learning-based face presentation attack detection (PAD) methods have gained increasing popularity. In this research, we propose a supervised contrastive learning approach to tackle the face anti-spoofing problem. Essentially, the latent space encoding is achieved through an encoder network using the contrastive loss function infused with the class label information. The proposed robust encoding is followed by a simple classifier to distinguish between a real and a spoof face. To the best of our knowledge, this is the first work that uses fully supervised contrastive learning for the two-dimensional (2D) face PAD task. The performance of the proposed method is evaluated on several face anti-spoofing datasets and the results clearly show the efficacy of the proposed approach compared to other contemporary methods.