Robust and Swift Iris Recognition at distance based on novel pupil segmentation
One of the most common and successful biometric frameworks is iris recognition, which has yielded promising results in systems of access control and identity authentication. Recent systems of iris recognition have concentrated on photos taken in non-ideal circumstances. Non-ideal imaging environment...
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| Format: | Article |
| Language: | English |
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Springer
2022-11-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157822003238 |
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| author | Ahmed Khudhur Nsaif Sawal Hamid Md. Ali Asama Kuder Nseaf Khider Nassif Jassim Ammar Al-Qaraghuli Riza Sulaiman |
| author_facet | Ahmed Khudhur Nsaif Sawal Hamid Md. Ali Asama Kuder Nseaf Khider Nassif Jassim Ammar Al-Qaraghuli Riza Sulaiman |
| author_sort | Ahmed Khudhur Nsaif |
| collection | DOAJ |
| description | One of the most common and successful biometric frameworks is iris recognition, which has yielded promising results in systems of access control and identity authentication. Recent systems of iris recognition have concentrated on photos taken in non-ideal circumstances. Non-ideal imaging environments involve the capture of iris pictures in motion, at a distance, or under near-infrared (NIR) or visible wavelength illumination, which introduces noise factors such as gaze deviation, lack of focus, and obstruction by hair, eyelids, lighting, specular reflections, and eyeglasses. Segmenting irises is still a challenging task for eye images taken under non-ideal circumstances for iris recognition, and is strongly dependent on pupil segmentation; to estimate a line of sight using the pupil centre method, pupil segmentation is essential. The quality of eye pictures often changes due to noise effects and the individual variance between human eyes, making segmentation of the pupil a tricky problem. To address this issue, we propose an efficacious, swift, and robust iris recognition method that is convenient for eye pictures under non-ideal conditions. In this paper, we present a novel pupil segmentation method based on a fusion of the multi-scale gray-level co-occurrence matrix (MSGLCM) and the multi-range circle Hough transform (MRRCHT) methods. Using this segmentation approach, the pupil texture region is extracted correctly and a Hough transform is then used for the outer iris region. To support the improvements in segmentation accuracy, we also apply pre- and post-processing operations. An evaluation of the proposed method is conducted in this paper using the CASIA v4.0 (Distance) and MMU V2 databases, which reflect the challenges of iris recognition from non-ideal pictures. Extensive experimental findings clarify the effectiveness of the suggested method for non-ideal iris pictures. |
| format | Article |
| id | doaj-art-a45bd415d20948bd9f66cad1c4cb8ed1 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-a45bd415d20948bd9f66cad1c4cb8ed12025-08-20T03:48:31ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-11-0134109184920610.1016/j.jksuci.2022.09.002Robust and Swift Iris Recognition at distance based on novel pupil segmentationAhmed Khudhur Nsaif0Sawal Hamid Md. Ali1Asama Kuder Nseaf2Khider Nassif Jassim3Ammar Al-Qaraghuli4Riza Sulaiman5Department of Electrical, Electronic, and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; Corresponding authors.Department of Electrical, Electronic, and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; Corresponding authors.Longo Faculty of Business, Humber College, Toronto, ON M8V 1K8, Canada; Institute of IR4.0, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, MalaysiaDepartment of Statistics, Faculty of Management and Economics, University of Wasit, Al-Kut 52001, IraqLongo Faculty of Business, Humber College, Toronto, ON M8V 1K8, Canada; Faculty of Applied Science and Technology, Sheridan College, Oakville, ON L6H 2L1, CanadaInstitute of IR4.0, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, MalaysiaOne of the most common and successful biometric frameworks is iris recognition, which has yielded promising results in systems of access control and identity authentication. Recent systems of iris recognition have concentrated on photos taken in non-ideal circumstances. Non-ideal imaging environments involve the capture of iris pictures in motion, at a distance, or under near-infrared (NIR) or visible wavelength illumination, which introduces noise factors such as gaze deviation, lack of focus, and obstruction by hair, eyelids, lighting, specular reflections, and eyeglasses. Segmenting irises is still a challenging task for eye images taken under non-ideal circumstances for iris recognition, and is strongly dependent on pupil segmentation; to estimate a line of sight using the pupil centre method, pupil segmentation is essential. The quality of eye pictures often changes due to noise effects and the individual variance between human eyes, making segmentation of the pupil a tricky problem. To address this issue, we propose an efficacious, swift, and robust iris recognition method that is convenient for eye pictures under non-ideal conditions. In this paper, we present a novel pupil segmentation method based on a fusion of the multi-scale gray-level co-occurrence matrix (MSGLCM) and the multi-range circle Hough transform (MRRCHT) methods. Using this segmentation approach, the pupil texture region is extracted correctly and a Hough transform is then used for the outer iris region. To support the improvements in segmentation accuracy, we also apply pre- and post-processing operations. An evaluation of the proposed method is conducted in this paper using the CASIA v4.0 (Distance) and MMU V2 databases, which reflect the challenges of iris recognition from non-ideal pictures. Extensive experimental findings clarify the effectiveness of the suggested method for non-ideal iris pictures.http://www.sciencedirect.com/science/article/pii/S1319157822003238Pupil segmentationIris recognitionIris segmentationIris biometric |
| spellingShingle | Ahmed Khudhur Nsaif Sawal Hamid Md. Ali Asama Kuder Nseaf Khider Nassif Jassim Ammar Al-Qaraghuli Riza Sulaiman Robust and Swift Iris Recognition at distance based on novel pupil segmentation Journal of King Saud University: Computer and Information Sciences Pupil segmentation Iris recognition Iris segmentation Iris biometric |
| title | Robust and Swift Iris Recognition at distance based on novel pupil segmentation |
| title_full | Robust and Swift Iris Recognition at distance based on novel pupil segmentation |
| title_fullStr | Robust and Swift Iris Recognition at distance based on novel pupil segmentation |
| title_full_unstemmed | Robust and Swift Iris Recognition at distance based on novel pupil segmentation |
| title_short | Robust and Swift Iris Recognition at distance based on novel pupil segmentation |
| title_sort | robust and swift iris recognition at distance based on novel pupil segmentation |
| topic | Pupil segmentation Iris recognition Iris segmentation Iris biometric |
| url | http://www.sciencedirect.com/science/article/pii/S1319157822003238 |
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