Efficient, accurate and fast pupil segmentation for pupillary boundary in iris recognition.

• Iris recognition is a prominent biometric system because of it is easy to use, high accuracy, fast identification time, accurate matching performance and difficult to spoof. • the iris recognition's accuracy can be affected because of reflection, motion blur and deformation of pupillary boundary. • Iris recognition may suffer if pupillary boundary is not correctly segmented. • Moreover, pupillary boundary can deform into uneven shape instead of circular shape. • After that, reflection in pupil area is eliminated with morphological closing. • The accurate pupillary boundary can be segmented after reflection elimination. Iris recognition is a robust biometric system—user-friendly, accurate, fast, and reliable. This biometric system captures information in a contactless manner, making it suitable for use during the COVID-19 pandemic. Despite its advantages such as high security and high accuracy, iris recognition still suffers from pupil deformation, motion blur, eyelids blocking, reflection occlusion and eyelashes obscure. If the pupillary boundary is not accurately segmented, iris recognition may suffer tremendously. Moreover, reflections in iris image may lead to an incorrect pupillary boundary segmentation. The segmentation accuracy can also be affected and reduced because of the presence of an unwanted noise created by the motion blur effect in iris image. Additionally, the pupillary boundary might change from circular shape to uneven or irregular shape because of the interference and obstruction in pupil region. Therefore, this work is carried out to determine an accurate, efficient and fast algorithm for the segmentation of pupillary boundary. First, the iris image is pre-processed with Wiener filter. Next, the respective iris image is assigned with a specific threshold. After that, the pixel property in iris image is computed to determine the pupillary boundary coordinates which are acquired from the measured pixel list and area in iris image. Finally, morphological closing is used to remove reflections in the inner region of pupil boundary. All experiments are implemented with CASIA v4 database and Matlab R2020a. [ABSTRACT FROM AUTHOR]