Browsing by Subject "Client-specific information"

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Open Access
Client-specific anomaly detection for face presentation attack detection
(Elsevier, 2020) Fatemifar, S.; Arashloo, Shervin Rahimzadeh; Awais, M.; Kittler, J.
One-class anomaly detection approaches are particularly appealing for use in face presentation attack detection (PAD), especially in an unseen attack scenario, where the system is exposed to novel types of attacks. This work builds upon an anomaly-based formulation of the problem and analyses the merits of deploying client-specific information for face spoofing detection. We propose training one-class client-specific classifiers (both generative and discriminative) using representations obtained from pre-trained deep Convolutional Neural Networks (CNN). In order to incorporate client-specific information, a distinct threshold is set for each client based on subject-specific score distributions, which is then used for decision making at the test time. Through extensive experiments using different one-class systems, it is shown that the use of client-specific information in a one-class anomaly detection formulation (both in model construction as well as decision boundary selection) improves the performance significantly. We also show that anomaly-based solutions have the capacity to perform as well or better than two-class approaches in the unseen attack scenarios. Moreover, it is shown that CNN features obtained from models trained for face recognition appear to discard discriminative traits for spoofing detection and are less capable for PAD compared to the CNNs trained for a generic object recognition task.
Open Access
Spoofing attack detection by anomaly detection
(Institute of Electrical and Electronics Engineers Inc., 2019) Fatemifar, S.; Arashloo, Shervin Rahimzadeh; Awais, M.; Kittler, J.
Spoofing 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.