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

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.