Improved Off-angle Iris segmentation using a novel distance-based loss function
Jitendra Sai Kota, Mahmut Karakaya · Gesellschaft für Informatik (GI) · 2025
Iris segmentation is an important step in determining downstream iris recognition performance. Most iris segmentation systems fail to reliably segment the iris images at extreme off angles that go beyond 30 degrees. Obtaining a manual ground truth for different extreme angles is both a costly and a time-consuming undertaking. In this paper, our aim is to automate this iris segmentation process for different extreme gaze angles, while minimizing the iris recognition performance loss. To address this, we propose an Iris segmentation method using a novel distance-based loss function and a residual-based ellipse fitting method to overcome these obstacles. The novel method shows an overall best performance of 0.9809 Area Under Curve (AUC) and an equal error rate (EER) of 0.0628, reducing the gap to the performance using ideal manual segmentation to 1.88% in terms of AUC and 0.056% in terms of EER.