Robust and Swift Iris Recognition at distance based on novel pupil segmentation

Abstract

One of the most common and successful biometric frameworks is iris recognition, which has yielded promising results in systems of access control and identity authentication. Recent systems of iris recognition have concentrated on photos taken in non-ideal circumstances. Non-ideal imaging environments involve the capture of iris pictures in motion, at a distance, or under near-infrared (NIR) or visible wavelength illumination, which introduces noise factors such as gaze deviation, lack of focus, and obstruction by hair, eyelids, lighting, specular reflections, and eyeglasses. Segmenting irises is still a challenging task for eye images taken under non-ideal circumstances for iris recognition, and is strongly dependent on pupil segmentation; to estimate a line of sight using the pupil centre method, pupil segmentation is essential. The quality of eye pictures often changes due to noise effects and the individual variance between human eyes, making segmentation of the pupil a tricky problem. To address this issue, we propose an efficacious, swift, and robust iris recognition method that is convenient for eye pictures under non-ideal conditions. In this paper, we present a novel pupil segmentation method based on a fusion of the multi-scale gray-level co-occurrence matrix (MSGLCM) and the multi-range circle Hough transform (MRRCHT) methods. Using this segmentation approach, the pupil texture region is extracted correctly and a Hough transform is then used for the outer iris region. To support the improvements in segmentation accuracy, we also apply pre- and post-processing operations. An evaluation of the proposed method is conducted in this paper using the CASIA v4.0 (Distance) and MMU V2 databases, which reflect the challenges of iris recognition from non-ideal pictures. Extensive experimental findings clarify the effectiveness of the suggested method for non-ideal iris pictures.