Open Access

ARTICLE

Adaptive Segmentation for Unconstrained Iris Recognition

by Mustafa AlRifaee¹, Sally Almanasra^2,*, Adnan Hnaif³, Ahmad Althunibat³, Mohammad Abdallah³, Thamer Alrawashdeh³

1 Faculty of Architecture and Design, Al-Zaytoonah University of Jordan, Amman, 11733, Jordan
2 Faculty of Computer Studies, Arab Open University, Riyadh, 11681, Saudi Arabia
3 Faculty of Science and Information Technology, Al-Zaytoonah University of Jordan, Amman, 11733, Jordan

* Corresponding Author: Sally Almanasra. Email:

(This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)

Computers, Materials & Continua 2024, 78(2), 1591-1609. https://doi.org/10.32604/cmc.2023.043520

Received 04 July 2023; Accepted 04 December 2023; Issue published 27 February 2024

Abstract

In standard iris recognition systems, a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture, look-and-stare constraints, and a close distance requirement to the capture device. When these conditions are relaxed, the system’s performance significantly deteriorates due to segmentation and feature extraction problems. Herein, a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments. First, the algorithm scans the whole iris image in the Hue Saturation Value (HSV) color space for local maxima to detect the sclera region. The image quality is then assessed by computing global features in red, green and blue (RGB) space, as noisy images have heterogeneous characteristics. The iris images are accordingly classified into seven categories based on their global RGB intensities. After the classification process, the images are filtered, and adaptive thresholding is applied to enhance the global contrast and detect the outer iris ring. Finally, to characterize the pupil area, the algorithm scans the cropped outer ring region for local minima values to identify the darkest area in the iris ring. The experimental results show that our method outperforms existing segmentation techniques using the UBIRIS.v1 and v2 databases and achieved a segmentation accuracy of 99.32 on UBIRIS.v1 and an error rate of 1.59 on UBIRIS.v2.

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Keywords

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