Fatemifar, S.Arashloo, Shervin RahimzadehAwais, M.Kittler, J.2020-01-282020-01-28201997814799813281520-6149http://hdl.handle.net/11693/52873Date of Conference: 12-17 May 2019Conference Name: 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019Spoofing attacks on biometric systems can seriously compromise their practical utility. In this paper we focus on face spoofing detection. The majority of papers on spoofing attack detection formulate the problem as a two or multiclass learning task, attempting to separate normal accesses from samples of different types of spoofing attacks. In this paper we adopt the anomaly detection approach proposed in [1], where the detector is trained on genuine accesses only using one-class classifiers and investigate the merit of subject specific solutions. We show experimentally that subject specific models are superior to the commonly used client independent method. We also demonstrate that the proposed approach is more robust than multiclass formulations to unseen attacks.EnglishFace anti-spoofingAnomaly detectionClient-specific informationOne-class classificationConvolutional neural networksConference Paper10.1109/ICASSP.2019.868225397814799813112379-190X