Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions

Fathee, Hala N.; Sahmoud, Shaaban

doi:10.1016/j.dsp.2021.103244

Cited by 8 publications

(2 citation statements)

References 96 publications

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“…In future work, we are planning to extend our work by including other iris colors that do not exist in the UBIRIS database such as green and red iris colors. In addition, optimizing the code of our algorithm and merging it with current CNN algorithms is another planned research work [38].…”

Section: Discussionmentioning

confidence: 99%

Exploiting Eye Colors for Better Iris Segmentation in Visible Wavelength Environments

Sahmoud

2024

International Journal of Advances in Engineering and Pure Sciences

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Iris segmentation is a crucial step in iris recognition systems. Iris segmentation in visible wavelength and unconstrained environments is more challenging than ‎segmenting iris images in ideal environments. This paper proposes a new iris segmentation method that exploits the color of human eyes to segment the iris region more accurately. While most of the current iris segmentation methods ignore the color of the iris or deal with ‎grayscale eye images directly, the proposed method takes benefits from iris color to simplify the iris segmentation step. In the first step, we estimate the expected iris center using Haar-like features. The iris color is detected and accordingly, a color-convenient segmentation algorithm is applied to find the iris region. Dealing separately with each iris color set significantly decreases the false segmentation errors and enhances the performance of the iris recognition system. The results of testing the proposed algorithm on the UBIRIS database demonstrate the robustness of our algorithm against different noise factors and non-ideal conditions.

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Section: Discussionmentioning

confidence: 99%

Exploiting Eye Colors for Better Iris Segmentation in Visible Wavelength Environments

Sahmoud

2024

International Journal of Advances in Engineering and Pure Sciences

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“…The edge detection results of remote sensing images directly affect the processing of remote sensing images such as image registration and recognition [2][3][4]. Edge detection is an extremely important step in target detection, and the effect of edge detection will directly affect the subsequent image processing [5,6]. Among them, differential operator method as the most common class of edge detection algorithms get widespread concern.…”

Section: Introductionmentioning

confidence: 99%

Edge detection algorithm for noisy remote sensing image using directional filter

Zhang

Fan

et al. 2023

J. Phys.: Conf. Ser.

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Remote sensing image edge detection is the basis of remote sensing image registration and recognition. In view of the noise formed in the process of remote sensing image acquisition, a new edge detection algorithm based on directional filter is proposed to improve the accuracy of remote sensing image registration and recognition. The directional filter is based on wavelet transform denoising, and three main features including scale, direction and shape of wave are introduced. The target edge can be identified more accurately in the noisy image by the directional filter, and the edge detection result image is obtained by binarization segmentation and non-maximum suppression. Through experimental comparison with other denoising detection algorithms, more accurate edge detection results can be obtained, and there are obvious advantages in operation efficiency.

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Identifying a Person in Mask: Fusion of Masked Face and Iris

Ahmad,

Pal,

Ganivada

2023

Lecture Notes in Computer Science

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Iris segmentation in uncooperative and unconstrained environments: State-of-the-art, datasets and future research directions

Cited by 8 publications

References 96 publications

Exploiting Eye Colors for Better Iris Segmentation in Visible Wavelength Environments

Exploiting Eye Colors for Better Iris Segmentation in Visible Wavelength Environments

Edge detection algorithm for noisy remote sensing image using directional filter

Identifying a Person in Mask: Fusion of Masked Face and Iris

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