IrisSeg: A Fast and Robust Iris Segmentation Framework for Non-Ideal Iris Images

This paper presents a state-of-the-art iris segmentation framework specifically for non-ideal irises. The framework adopts coarse-to-fine strategy to localize different boundaries. In the approach, pupil is coarsely detected using an iterative search method exploiting dynamic thresholding and multiple local cues. The limbic boundary is first approximated in polar space using adaptive filters and then refined in Cartesian space. The framework is quite robust and unlike the previously reported works, does not require tuning of parameters for different databases. The segmentation accuracy (SA) is evaluated using well known measures; precision, recall and F-measure, using the publicly available ground truth data for challenging iris databases; CASIAV4-Interval, ND-IRIS-0405, and HTD. In addition, the approach is also evaluated on highly challenging periocular images of FOCS database. The validity of proposed framework is also ascertained by providing comprehensive comparisons with classical approaches as well as state-of-the-art methods such as; CA HT, WAHET, IFFP, GST and Osiris v4.1. The results demonstrate that our approach provides significant improvements in segmentation accuracy as well as in recognition performance that too with lower computational complexity.