Filterable Sample Consensus Based on Angle Variance for Pupil Segmentation

Pupil segmentation is a first and important topic of iris recognition, identity recognition and eye movement information extraction for mental analysis. However, due to the negative effects of eyelash occlusion, eyelid occlusion and off-gaze deflection, making a precise pupil segmentation is a difficult task. Therefore, we propose a precise and robust algorithm for pupil segmentation, namely Angle Variance based Filterable Sample Consensus (AVBFSC), which is composed of an outlier filter and a boundary locator. The outlier filter can eliminate negative effects mentioned above, and also a best pupil segmentation is performed with our boundary locator, which learns a circular mathematical modeling by selecting sub-samples from pupil edge pixels randomly. Experiment results in comparison with state-of-the-art methods on CASIA-Iris-V4-Interval dataset, indicate that our algorithm achieved best performance, that is, Accuracy of 98.99%, False Acceptance Rate (FAR) of 2.09% and Genuine Acceptance Rate (GAR) of 98.54%. In addition, it also has robust results under the condition of specific non-ideal scenes from CASIA-Iris-V4-Lamp dataset including dark light conditions, eyelash occlusion, eyelid occlusion and off-gaze deflection