Particle Swarm And Pattern Search Optimisation Of An Ensemble Of Face Anomaly Detectors

While the remarkable advances in face matching render face biometric technology more widely applicable, its successful deployment may be compromised by face spoofing. Recent studies have shown that anomaly-based face spoofing detectors offer an interesting alternative to the multiclass counterparts by generalising better to unseen types of attack. In this work, we investigate the merits of fusing multiple anomaly spoofing detectors in the unseen attack scenario via a Weighted Averaging (WA) and client-specific design. We propose to optimise the parameters of WA by a two-stage optimisation method consisting of Particle Swarm Optimisation (PSO) and the Pattern Search (PS) algorithms to avoid the local minimum problem. Besides, we propose a novel scoring normalisation method which could be effectively applied in extreme cases such as heavy-tailed distributions. We evaluate the capability of the proposed system on publicly available face anti-spoofing databases including Replay-Attack, Replay-Mobile and Rose-Youtu. The experimental results demonstrate that the proposed fusion system outperforms the majority of anomaly-based and state-of-the-art multiclass approaches.