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.

使用虹膜和眼周区域作为生物特征已被广泛研究，主要是由于虹膜特征的奇异性以及当图像分辨率不足以提取虹膜信息时使用眼周区域。除了提供有关个人身份的信息之外，还可以探索从这些特征中提取的特征以获得其他信息，例如个人的性别、吸毒的影响、隐形眼镜的使用、欺骗等。这项工作对为视觉识别而创建的数据库进行了调查，详细说明了它们的协议以及它们的图像是如何获取的。我们还描述和讨论了最流行的视觉识别竞赛（比赛），突出显示仅使用虹膜特征并融合虹膜和眼周区域信息即可获得最佳结果的提交算法。最后，我们描述了一些将深度学习技术应用于视觉识别的相关工作，并指出了新的挑战和未来的方向。考虑到眼部数据库数量众多，而且每个数据库通常都是针对特定问题而设计的，我们相信这项调查可以对眼部生物识别技术的挑战提供一个广泛的概述。