Iris segmentation is a vital forepart module in iris recognition because it isolates the valid image region used for subsequent processing such as feature extraction. Traditional iris segmentation methods often involve an exhaustive search in a certain large parameter space, which is time consuming and sensitive to noise. Compared to traditional methods, this paper presents a novel algorithm for accurate and fast iris segmentation. A gray histogram-based adaptive threshold is used to generate a binary image, followed by connected component analysis, and rough pupil is separated. Then a strategy of RANSAC (Random sample consensus) is adopted to refine the pupil boundary. We present Valley Location of Radius-Gray Distribution (VLRGD) to detect the weak iris outer boundary and fit the edge. Experimental results on the popular iris database CASIA-Iris V4-Lamp demonstrate that the proposed approach is accurate and efficient.
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