A Robust Iris Segmentation Algorithm Based on Pupil Region for Visible Wavelength Environments

Fathee, Hala N.; Sahmoud, Shaaban; Abdul–Jabbar, Jassim M.

doi:10.1109/isriti51436.2020.9315343

Cited by 1 publication

(1 citation statement)

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“…The integro-differential technique to segmentation was found to be 91.39 percent accurate, whereas the Hough transform approach was found to be 93.06 percent accurate [13]. In 2020 Fathee et al [14] proposed a new segmentation algorithm to segment iris images that captured in visible wavelength environments. This algorithm starts with the area of the iris that is easiest to recognize, the black, circular area known as the pupil, and reduces the deterioration and noise from there.…”

Section: Introductionmentioning

confidence: 99%

Enhance iris segmentation method for person recognition based on image processing techniques

2023

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The limitation of traditional iris recognition systems to process iris images captured in unconstraint environments is a breakthrough. Automatic iris recognition has to face unpredictable variations of iris images in real-world applications. For example, the most challenging problems are related to the severe noise effects that are inherent to these unconstrained iris recognition systems, varying illumination, obstruction of the upper or lower eyelids, the eyelash overlap with the iris region, specular highlights on pupils which come from a spot of light during captured the image, and decentralization of iris image which caused by the person's gaze. Iris segmentation is one of the most important processes in iris recognition. Due to the different types of noise in the eye image, the segmentation result may be erroneous. To solve this problem, this paper develops an efficient iris segmentation algorithm using image processing techniques. Firstly, the outer boundary segmentation of the iris problem is solved. Then the pupil boundary is detected. Testes are done on the Chinese Academy of Sciences' Institute of Automation (CASIA) database. Experimental results indicate that the proposed algorithm is efficient and effective in terms of iris segmentation and reduction of time processing. The accuracy results for both datasets (CASIA-V1 and V4) are 100% and 99.16 respectively.

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Section: Introductionmentioning

confidence: 99%