Real time iris segmentation quality evaluation using medoids

•We propose a novel medoids based iris segmentation-quality evaluation model, as a robust alternative to real-time iris segmentation-quality evaluation.•It uses eccentricity measure and annulus radii ratio to screen out severe segmentation failure prior to k-medoids clustering.•A comparative evaluation using CASIA_v4, IITD_v2, UBIRIS_v2 and a novel iris dataset Biometric Vision and computing (BVC_v1s1) iris dataset was performed.•The proposed model consistently recorded the least classification error-rate across diverse iris datasets and 95.27% classification accuracy rate on one evaluating iris-datasets.