Robust Video Based Iris Segmentation System in Less Constrained Environments

One of the key challenges in traditional iris recognition systems is that they require substantial user cooperation. Several restrictions are imposed on positioning and motion of the subject during the image acquisition process so that an image of high quality can be captured. On the other hand, videos captured at a distance and on the move are less intrusive and more appealing to users. However, this extra convenience comes at a cost. Such videos suffer from significant degradation and are often of poor quality compared to images captured in controlled environments. In this work, we present a video based iris segmentation system for processing of images taken in less constrained environments. In the first part, frame alignment of face videos is performed for reliable and efficient extraction of the eye regions in Near Infrared (NIR) videos. In the second section, we propose a new iris segmentation method aimed particularly at eye images captured in challenging environments. Reflections and out of frame iris regions are in-painted. A region based segmentation method is proposed for accurate eyelid detection in images with variable illumination and significant blur. Eyelashes are divided into two categories and eliminated. Experiments carried out on the Multiple Biometric Grand Challenge (MBGC) dataset demonstrate that the proposed system achieves higher accuracy than other recent state of the art video based iris segmentation techniques developed for less constrained environments.