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A survey on data-driven iris spoof detectors: state-of-the-art, open issues and future perspectives

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Abstract

In the modern era of computing, the iris-based biometric systems are gaining significant attention for secured and automatic human authentication. However, past decades have witnessed numerous spoofing assaults on these iris-based recognition systems where an attacker impersonates an exact replica of biometrical information of the genuine user. Particularly, these direct attacks are targeted on the iris sensor module of the biometric system by presenting the fake artefacts of a bonafide iris trait. With the emergence of data-driven paradigm (i.e. handcrafted feature learners such as support vector machine (SVM), decision tree (DT), k-nearest neighbor (KNN), ensembles, etc. or automatic image features extraction-based classifiers such as convolutional neural networks (CNN), generative adversial networks (GAN)), mitigating these iris spoof attacks has become comparatively an easier and accurate task of computer vision. An iris spoof detector (ISD) is a mechanism through which the vitality of a presented iris trait is measured intelligently by classifying it as genuine or counterfeit. In this study, we explicate a taxonomy-based comparative analysis of state-of-the-art (SOTA) ISDs that employ machine learning or deep learning-based approaches. We expound a novel taxonomy for classifying ISDs based on underlying criterion such as feature type, learning algorithm, pre-trained models, data augmentation, hybrid, etc. Furthermore, we investigate and analyze various benchmark datasets employed in the various data-driven iris spoof detectors (D2ISD). We also illustrate prominent performance evaluation protocols that are widely adopted in the SOTA approaches. Though, pioneer contributions related to D2ISD is reported in the literature, but several potential open research problems still exist, that requisite a futuristic attention of the investigators in this active field of research.

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Data availability

The datasets used/generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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  1. Department of Computer Science and Information Technology, Central University of Jammu, Samba, 181143, India

    Palak Verma, Arvind Selwal & Deepika Sharma

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Verma, P., Selwal, A. & Sharma, D. A survey on data-driven iris spoof detectors: state-of-the-art, open issues and future perspectives. Multimed Tools Appl 82, 19745–19792 (2023). https://doi.org/10.1007/s11042-022-14014-4

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