Client-specific anomaly detection for face presentation attack detection

•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.