Client-specific anomaly detection for face presentation attack detection

Highlights：

• We propose an anomaly-based face spoofing detection solution using representations derived by different CNN architectures.

• By training the anomaly detection systems on genuine access data only, we avoid overfitting to any specific face spoofing attack data, and achieve improved robustness to novel types of attacks.

• We investigate the merits of exploiting client-specific information in both, building anomaly-based spoofing detectors, as well as setting client-specific thresholds.

• By conducting experiments on three benchmarking anti-spoofing datasets, we demonstrate that the proposed client-specific anomaly detection solution delivers superior performance compared to the state-of-the-art approaches in unseen attack scenarios.