A Hybrid Approach for Segmenting Non-ideal Iris Images Using CGAN and Geometry Constraints

Abstract

The prevalence of personal mobile devices makes iris authentication being more and more popular. Accurate iris segmentation is critical for authentication. However, it is very challenging, due to iris images captured by mobile and handheld devices may exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. Moreover, mobile devices may be equipped with visible light cameras rather than near-infrared (NIR) light cameras, which makes iris segmentation susceptible to the noise of visible light. We propose an accurate iris image segmentation approach, which takes advantages of both Conditional Generative Adversarial Network (CGAN) and geometry-based optimization. First, we design a CGAN which force the generator to produce better segmentation corresponds to the original image, a comparatively accurate prediction of iris region can be obtained. Second, a series of geometry-based optimization schemes is introduced to refine the prediction results, where elliptical Hough transform and boundary piecewise fitting are performed on the inner and outer boundary of predicted iris regions, respectively. We performed experiments on three non-ideal iris datasets of visible light and NIR environments. The segmentation accuracy is evaluated using error rate, intersection over union and F-score. Experimental results demonstrate that the proposed approach provides significant performance improvements comparing with the state-of-art methods, OSIRIS and IrisSeg.