A robust segmentation approach to iris recognition based on video

One of the key problems of conventional iris recognition methods is that they are based on processing single iris image and require good image quality as an essential condition. These requisites entail considerable constraints on users for taking iris images. Video based iris recognition can provide convenience and time efficiency to the subjects with undemanding restrains during iris acquisition. These videos which are usually taken of moving subjects at a distance, although convenient to the user, may result in an acquisition approach that can certainly introduce unexpected noise sources to the iris images, impacting as a consequence the verification accuracy for iris recognition. With this dilemma, this study introduces a new segmentation approach for video based iris recognition. The proposed approach consists of two steps. The first step consists of a video frame selection which is to obtain qualified frames from near infrared (NIR) video, where the subjects' eye images are extracted based on the featured reflection spots generated by the specified video camera. The second step is based on an iris segmentation designed to isolate the iris from the eye image. Since iris images obtained from NIR video may suffer from different kinds of noise effects, a new strategy is proposed for iris segmentation to overcome such noise effects and process effectively the eye images (frames) in the presence of noise. More importantly, the proposed iris segmentation strategy would not only separate the iris part from the sclera and pupil, but also can identify the extraneous overlapping parts caused by eyelids, eyelashes, and reflection spots.