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Ocular recognition databases and competitions: a survey

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Abstract

The use of the iris and periocular region as biometric traits has been extensively investigated, mainly due to the singularity of the iris features and the use of the periocular region when the image resolution is not sufficient to extract iris information. In addition to providing information about an individual’s identity, features extracted from these traits can also be explored to obtain other information such as the individual’s gender, the influence of drug use, the use of contact lenses, spoofing, among others. This work presents a survey of the databases created for ocular recognition, detailing their protocols and how their images were acquired. We also describe and discuss the most popular ocular recognition competitions (contests), highlighting the submitted algorithms that achieved the best results using only iris trait and also fusing iris and periocular region information. Finally, we describe some relevant works applying deep learning techniques to ocular recognition and point out new challenges and future directions. Considering that there are a large number of ocular databases, and each one is usually designed for a specific problem, we believe this survey can provide a broad overview of the challenges in ocular biometrics.

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Acknowledgements

This work was supported by Grants from the National Council for Scientific and Technological Development (CNPq) (Grant Numbers 428333/2016-8, 313423/2017-2 and 306684/2018-2), and the Coordination for the Improvement of Higher Education Personnel (CAPES) (Social Demand Program), both funding agencies from Brazil.

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  1. Department of Informatics, Federal University of Paraná, Curitiba, Brazil

    Luiz A. Zanlorensi, Rayson Laroca, Luiz S. Oliveira & David Menotti

  2. Computing Department, Federal University of Ouro Preto, Ouro Preto, Brazil

    Eduardo Luz

  3. Postgraduate Program in Informatics, Pontifical Catholic University of Paraná, Curitiba, Brazil

    Alceu S. Britto Jr.

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  1. Luiz A. Zanlorensi
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Zanlorensi, L.A., Laroca, R., Luz, E. et al. Ocular recognition databases and competitions: a survey. Artif Intell Rev 55, 129–180 (2022). https://doi.org/10.1007/s10462-021-10028-w

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