Deep Learning-based Iris Quality Assessment for Images Sourced from Websites and Social Media

Recent studies have demonstrated the feasibility of performing iris recognition by using ocular images collected from websites and social media. Unfortunately, these images often exhibit significant non-idealities due to the challenging conditions under which they are captured. Dedicated quality assessment methods could improve the accuracy and robustness of current biometric recognition technologies by identifying and discarding insufficient quality samples. However, no existing quality assessment method specifically addresses the challenges posed by samples collected in this application context. In this paper, we propose a quality assessment method based on deep neural networks, designed to address the non-idealities commonly found in ocular images collected from websites and social media. We explore three configurations of the method that leverage information from the iris localization and segmentation stages of a biometric system: (i) direct quality assessment of raw ocular images, (ii) quality assessment using iris localization data, and (iii) quality assessment incorporating both localization and segmentation data. We validated the proposed method using datasets of ocular images acquired in unconstrained conditions. The experimental results demonstrate that it significantly improves the recognition accuracy of state-of-the-art biometric systems, reducing the Equal Error Rate from 18.5% to 11.8% for I-SOCIAL-DB. Furthermore, a cross-database evaluation proves the robustness of our approach under heterogeneous and non-ideal acquisition conditions.