Soroush Fatemifar

Soroush Fatemifar

Postgraduate Research Student

Academic and research departments

Centre for Vision, Speech and Signal Processing (CVSSP).

My publications


Fatemifar Soroush, Arashloo Shervin Rahimzadeh, Awais Muhammad, Kittler Josef (2019) Spoofing Attack Detection by Anomaly Detection,Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019) pp. 8464-8468 Institute of Electrical and Electronics Engineers (IEEE)
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.
Fatemifar Soroush, Awais Muhammad, Rahimzadeh Shervin, Kittler Josef Combining Multiple one-class Classifiers for Anomaly based Face Spoofing Attack Detection,2019 International Conference on Biometrics (ICB)
One-class spoofing detection approaches have been an
effective alternative to the two-class learners in the face presentation attack detection particularly in unseen attack scenarios. We propose an ensemble based anomaly detection approach applicable to one-class classifiers. A new score normalisation method is proposed to normalise the output of individual outlier detectors before fusion. To comply with the accuracy and diversity objectives for the component classifiers, three different strategies are utilised to build a pool of anomaly experts. To boost the performance, we also make use of the client-specific information both in the design of individual experts as well as in setting a distinct threshold for each client. We carry out extensive experiments
on three face anti-spoofing datasets and show that
the proposed ensemble approaches are comparable superior
to the techniques based on the two-class formulation
or class-independent settings.
Fatemifar Soroush, Arashloo Shervin Rahimzadeh, Awais Muhammad, Kittler Josef (2020) Client-Specific Anomaly Detection for Face Presentation Attack Detection,Pattern Recognition Elsevier
The one-class anomaly detection approach has previously been found to be effective in face presentation attack detection, especially in an \textit{unseen} attack scenario, where the system is exposed to novel types of attacks. This work follows the same anomaly-based formulation of the problem and analyses the merits of deploying \textit{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. Next, based on subject-specific score distributions, a distinct threshold is set for each client, which is then used for decision making regarding a test query. 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 threshold tuning) improves the performance significantly. In addition, it is demonstrated that the same set of deep convolutional features used for the recognition purposes is effective for face presentation attack detection in the class-specific one-class anomaly detection paradigm.