Probe Visible Iris Area as a Dominant Predictor of Iris Recognition Performance Under Dilation and Occlusion

With the increasing adoption of iris recognition systems and the expansion of large-scale enrollment databases, there is a growing need to efficiently assess iris image quality at the time of acquisition. Image quality may degrade for several reasons, such as eyelid occlusion or pupil dilation. Although previous studies have shown that occlusion and changes in the pupil-to-iris ratio negatively impact recognition performance, these investigations were typically limited by small sample sizes and did not examine the combined effects of eyelid and pupil variations. In this study, we analyze both dilation and eyelid occlusion using a large dataset of 827 distinct irises and demonstrate a strong correlation between probe image visible iris area and the Hamming distance of iris code pairs. Building on this finding, we develop a probe-adaptive row-weighted Hamming distance model that scales per-row contributions by a spline function of probe covariates, significantly reducing the false nonmatch rate on the held-out evaluation split. These results establish visible iris area as a robust, acquisition-time quality signal and demonstrate that it can be directly operationalized to improve match confidence without modifying the underlying iris recognition pipeline.