Iris segmentation for non-cooperative recognition systems

Segmenting iris texture from an input image is an important step for recognising iris pattern. It is still a difficult task to localise available texture regions from non-ideal iris images captured in non-cooperative situations such as lighting variations, on-the-move and off-angle view. To address this problem, this study presents a novel algorithm for accurate and fast iris segmentation. An adaptive mean shift procedure is built to find the rough position of the iris centre. According to the localisation result, a circle is set as the initial iris contour. After combining the statistical texture prior modelled as Markov random field, a merged active contour model is established in terms of level set theory. Under the MAC model, the initial contour is iteratively driven to real iris boundaries. During the curve evolving process, eyelids, eyelashes, reflections and shadows can be simultaneously detected and labelled in iris regions. The novelty of the proposed method mainly includes developing a new modified mean shift procedure for fast and robust iris localisation, and successfully incorporating the local probabilistic prior, boundary and region information into the designed active contour model for accurate texture segmentation. Extensive experimental results on various challenging iris images show that our method can effectively and accurately perform iris segmentation with low computational complexity and has very promising applications in non-cooperative recognition systems.