I-SOCIAL-DB: A labeled database of images collected from websites and social media for Iris recognition

Labati R. Donida; Genovese Angelo; Piuri Vincenzo; Scotti Fabio; Vishwakarma Sarvesh

摘要

People upload daily a huge number of portrait face pictures on websites and social media, which can be processed using biometric systems based on the face characteristics to perform an automatic recognition of the individuals. However, the performance of face recognition approaches can be limited by negative factors as aging, occlusions, rotations, and uncontrolled expressions. Nevertheless, the constantly increasing quality and resolution of the portrait pictures uploaded on websites and social media could permit to overcome these problems and improve the robustness of biometric recognition methods by enabling the analysis of additional traits, like the iris. To point the attention of the research community to the possible use of iris-based recognition techniques for images uploaded on websites and social media, we present a public image dataset called I-SOCIAL-DB (Iris Social Database). This dataset is composed of 3,286 ocular regions, extracted from 1,643 high-resolution face images of 400 individuals, collected from public websites. For each ocular region, a human expert extracted the coordinates of the circles approximating the inner and outer iris boundaries and performed a pixelwise segmentation of the iris contours, occlusions, and reflections. This dataset is the first collection of ocular images from public websites and social media, and one of the biggest collections of manually segmented ocular images in the literature. In this paper, we also present a qualitative analysis of the samples, a set of testing protocols and figures of merit, and benchmark results achieved using publicly available iris segmentation and recognition algorithms. We hope that this initiative can give a new test tool to the biometric research community, aiming to stimulate new studies in this challenging research field. (C) 2020 Elsevier B.V. All rights reserved.

机译：人们在网站和社交媒体上传每日大量的肖像脸图片，可以使用基于面部特征来处理自动识别个体的生物识别系统。然而，面部识别方法的性能可以受到衰老，闭塞，旋转和不受控制的表达式的负因子的限制。尽管如此，在网站和社交媒体上上传的肖像图片的不断提高的质量和分辨率可以允许克服这些问题，并通过使额外特征的分析如虹膜来改善生物识别方法的鲁棒性。要指出研究界的注意力，可以使用基于IRIS的识别技术，用于上传在网站和社交媒体上的图像，我们呈现了一个名为I-Social DB（IRIS Social Database）的公共图像数据集。该数据集由3,286个眼部区域组成，从公共网站收集的400个人的1,643个高分辨率面部图像中提取。对于每个眼部区域，人类专家提取近似内虹膜边界的圆圈的坐标，并执行虹膜轮廓，闭塞和反射的像素的分割。该数据集是来自公共网站和社交媒体的第一个OCURING图像的集合，以及文献中的手动分段眼图像的最大系列。在本文中，我们还对样品的定性分析，一系列的测试协议和数字，以及利用公共IRIS分割和识别算法实现的基准结果。我们希望这一倡议能够为生物识别研究界提供新的测试工具，旨在刺激在这一具有挑战性的研究领域的新研究。（c）2020 Elsevier B.v.保留所有权利。

I-SOCIAL-DB: A labeled database of images collected from websites and social media for Iris recognition

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