Work

Domain Generalization and Domain Adaptation Approaches for Face Anti-Spoofing: A Survey

Year: 2025

Type: article

Abstract: Face Anti-Spoofing (FAS), also referred to as Face Liveness, has emerged as a rapidly evolving field of research in Computer Vision. Closely tied to Face Recognition, its primary objective is to authenticate an identity by verifying its authenticity. However, safeguarding against diverse types of spoof attacks poses significant challenges due to the vast range of spoofing methods, capture devices, and environments. To mitigate this issue, researchers frequently employ Domain Generalization and Domain Adaptation approaches. This paper presents a comprehensive review of the latest deep learning-based FAS techniques that have achieved state-of-the-art results using the leave-one-domain-out protocol and the OCIM evaluation method, a common benchmark in both Domain Generalization and Domain Adaptation FAS. Finally, we conclude this survey by exploring potential new research directions in FAS.

Source: IEEE Access

DOI: 10.1109/access.2025.3592034

Authors: Hanry Ham, Ari Saptawijaya, Aniati Murni Arymurthy

Institution: University of Indonesia

Language: English

Topic: Biometric Identification and Security

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funder: Universitas Indonesia