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Abstract
With increasing needs in security systems, iris recognition is an important technique as one of the most reliable solutions for biometrics-based identification systems. However, not all of the iris images acquired from the device are in-focus and sharp enough for recognition. Thus, the poor quality of iris images has serious influence on the accuracy of iris recognition. Sometimes these images are not good enough due to a variety of factors: defocus blur, motion blur, eyelid occlusion and eyelash occlusion. This paper presents an approach for quality assessment of iris images, which can select the high quality iris images from the image sequences to be used in iris recognition systems. First, the gradient information of the iris regions (64 × 64) adjoining the pupil on the right and left sides is calculated to distinguish the blurred images from the in-focus images. Next, the valid iris regions are employed to discriminate between the occluded images and useful images. We present underlying theory as well as experimental results from both the CASIA iris database and the database provided for the iris challenge evaluation (ICE). The results show that this evaluation approach can actually reflect the real quality of iris images and significantly improve the overall performance of the iris recognition systems.