Open Access iconOpen Access

ARTICLE

crossmark

Adaptive Segmentation for Unconstrained Iris Recognition

Mustafa AlRifaee1, Sally Almanasra2,*, 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: 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

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.

Keywords


Cite This Article

APA Style
AlRifaee, M., Almanasra, S., Hnaif, A., Althunibat, A., Abdallah, M. et al. (2024). Adaptive segmentation for unconstrained iris recognition. Computers, Materials & Continua, 78(2), 1591-1609. https://doi.org/10.32604/cmc.2023.043520
Vancouver Style
AlRifaee M, Almanasra S, Hnaif A, Althunibat A, Abdallah M, Alrawashdeh T. Adaptive segmentation for unconstrained iris recognition. Comput Mater Contin. 2024;78(2):1591-1609 https://doi.org/10.32604/cmc.2023.043520
IEEE Style
M. AlRifaee, S. Almanasra, A. Hnaif, A. Althunibat, M. Abdallah, and T. Alrawashdeh "Adaptive Segmentation for Unconstrained Iris Recognition," Comput. Mater. Contin., vol. 78, no. 2, pp. 1591-1609. 2024. https://doi.org/10.32604/cmc.2023.043520



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 632

    View

  • 200

    Download

  • 0

    Like

Share Link