Browsing by Author "Kittler, J."
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Item 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.Item Open Access In Press, Corrected Proof: Multi-target regression via non-linear output structure learning(Elsevier, 2021-12-18) Arashloo, Shervin Rahimzadeh; Kittler, J.The problem of simultaneously predicting multiple real-valued outputs using a shared set of input variables is known as multi-target regression and has attracted considerable interest in the past couple of years. The dominant approach in the literature for multi-target regression is to capture the dependencies between the outputs through a linear model and express it as an output mixing matrix. This modelling formalism, however, is too simplistic in real-world problems where the output variables are related to one another in a more complex and non-linear fashion. To address this problem, in this study, we propose a structural modelling approach where the correlations between output variables are modelled using a non-linear approach. In particular, we pose the multi-target regression problem as one of vector-valued composition function learning in the reproducing kernel Hilbert space and propose a non-linear structure learning approach to capture the relationship between the outputs via an output kernel. By virtue of using a non-linear output kernel function, the proposed approach can better discover non-linear dependencies among targets for improved prediction performance. An extensive evaluation conducted on different databases reveals the benefits of the proposed multi-target regression technique against the baseline and the state-of-the-art methods.Item Open Access Multi-target regression via non-linear output structure learning(Elsevier BV, 2021-12-18) Arashloo, Shervin Rahimzadeh; Kittler, J.The problem of simultaneously predicting multiple real-valued outputs using a shared set of input variables is known as multi-target regression and has attracted considerable interest in the past couple of years. The dominant approach in the literature for multi-target regression is to capture the dependencies between the outputs through a linear model and express it as an output mixing matrix. This modelling formalism, however, is too simplistic in real-world problems where the output variables are related to one another in a more complex and non-linear fashion. To address this problem, in this study, we propose a structural modelling approach where the correlations between output variables are modelled using a non-linear approach. In particular, we pose the multi-target regression problem as one of vector-valued composition function learning in the reproducing kernel Hilbert space and propose a non-linear structure learning approach to capture the relationship between the outputs via an output kernel. By virtue of using a non-linear output kernel function, the proposed approach can better discover non-linear dependencies among targets for improved prediction performance. An extensive evaluation conducted on different databases reveals the benefits of the proposed multi-target regression technique against the baseline and the state-of-the-art methodsItem Open Access Robust one-class kernel spectral regression(IEEE, 2021-03) Arashloo, Shervin Rahimzadeh; Kittler, J.The kernel null-space technique is known to be an effective one-class classification (OCC) technique. Nevertheless, the applicability of this method is limited due to its susceptibility to possible training data corruption and the inability to rank training observations according to their conformity with the model. This article addresses these shortcomings by regularizing the solution of the null-space kernel Fisher methodology in the context of its regression-based formulation. In this respect, first, the effect of the Tikhonov regularization in the Hilbert space is analyzed, where the one-class learning problem in the presence of contamination in the training set is posed as a sensitivity analysis problem. Next, the effect of the sparsity of the solution is studied. For both alternative regularization schemes, iterative algorithms are proposed which recursively update label confidences. Through extensive experiments, the proposed methodology is found to enhance robustness against contamination in the training set compared with the baseline kernel null-space method, as well as other existing approaches in the OCC paradigm, while providing the functionality to rank training samples effectively.Item 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.