Abstract
The deployment of an iris recognition framework highlighted the relevance of developing a presentation attack detection (PAD) approach. The objective of this approach is to verify whether the acquired iris pattern is real or not. Impersonation attacks against an iris recogniser could be carried out by counterfeiting the natural iris patterns with fake replicas. Such replication can take several forms, with the contact lens attack being the most challenging. In an iris recognition at a distance (IAAD) scenario, this paper describes the design of a lightweight cosmetic contact lens detection system. The approach employs the popular Binarized Statistical Image Features (BSIF) descriptor and evaluates a separate set of two classifiers (Support Vector Machines (SVM), and multilayer perceptrons (MLP)) over a bank of 120 different BSIF encodings. As main novelty with respect to previous approaches, our descriptors are obtained from the normalised iris pattern, and not directly from the eye image. The resulting set of 232 models, built using the Notre Dame (NDCLD’15) dataset, was ranked by their performance on a validation set built using the Cogent subset of the IIITD dataset and then added one at a time to create a classification ensemble. Using an ensemble with only three SVM-based classifiers, we have obtained a correct classification rate (CCR) of 97,30%.
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https://tinyurl.com/wssberz (Accessed on March 28, 2023).
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Acknowledgements
This work has been partly supported by grant CPP2021-008931 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR, and by projects TED2021-131739B-C21 and PDC2022-133597-C42, funded by the Gobierno de España and FEDER funds.
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Romero-Garcés, A., Ruiz-Beltrán, C., Marfil, R., Bandera, A. (2023). Lightweight Cosmetic Contact Lens Detection System for Iris Recognition at a Distance. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 750. Springer, Cham. https://doi.org/10.1007/978-3-031-42536-3_24
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