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A comprehensive investigation into sclera biometrics: a novel dataset and performance study

  • S.I. : Developing nature-inspired intelligence by neural systems
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Abstract

The area of ocular biometrics is among the most popular branches of biometric recognition technology. This area has long been dominated by iris recognition research, while other ocular modalities such as the periocular region or the vasculature of the sclera have received significantly less attention in the literature. Consequently, ocular modalities beyond the iris are not well studied and their characteristics are today still not as well understood. While recent needs for more secure authentication schemes have considerably increased the interest in competing ocular modalities, progress in these areas is still held back by the lack of publicly available datasets that would allow for more targeted research into specific ocular characteristics next to the iris. In this paper, we aim to bridge this gap for the case of sclera biometrics and introduce a novel dataset designed for research into ocular biometrics and most importantly for research into the vasculature of the sclera. Our dataset, called Sclera Blood Vessels, Periocular and Iris (SBVPI), is, to the best of our knowledge, the first publicly available dataset designed specifically with research in sclera biometrics in mind. The dataset contains high-quality RGB ocular images, captured in the visible spectrum, belonging to 55 subjects. Unlike competing datasets, it comes with manual markups of various eye regions, such as the iris, pupil, canthus or eyelashes and a detailed pixel-wise annotation of the complete sclera vasculature for a subset of the images. Additionally, the datasets ship with gender and age labels. The unique characteristics of the dataset allow us to study aspects of sclera biometrics technology that have not been studied before in the literature (e.g. vasculature segmentation techniques) as well as issues that are of key importance for practical recognition systems. Thus, next to the SBVPI dataset we also present in this paper a comprehensive investigation into sclera biometrics and the main covariates that affect the performance of sclera segmentation and recognition techniques, such as gender, age, gaze direction or image resolution. Our experiments not only demonstrate the usefulness of the newly introduced dataset, but also contribute to a better understanding of sclera biometrics in general.

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Notes

  1. To ensure reproducibility of our results we make all training code, model definitions, and learned weights publicly available from http://sclera.fri.uni-lj.si/.

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Acknowledgements

This research was supported in parts by the ARRS (Slovenian Research Agency) Research Programme P2-0250 (B) Metrology and Biometric Systems and the ARRS Research Programme P2-0214 (A) Computer Vision. The authors would also like to thank the Nvidia corporation for donating the Titan V GPU, which allowed us to evaluate CNN-based approaches more efficiently.

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Authors and Affiliations

  1. Computer Vision Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000, Ljubljana, Slovenia

    Matej Vitek, Peter Rot & Peter Peer

  2. Laboratory for Machine Intelligence, Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000, Ljubljana, Slovenia

    Peter Rot & Vitomir Štruc

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  1. Matej Vitek
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Correspondence to Matej Vitek.

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Vitek, M., Rot, P., Štruc, V. et al. A comprehensive investigation into sclera biometrics: a novel dataset and performance study. Neural Comput & Applic 32, 17941–17955 (2020). https://doi.org/10.1007/s00521-020-04782-1

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