Enhance non-ideal iris recognition system from NIR Iris video

Nur Khder Nseaf, Azrulhizam Shapi`i, Asama Kuder Nseaf, Azizah Jaafar, Khider Nassif Jassim, Ahmed Khudhur Nsaif

Research output: Contribution to journalArticle

Abstract

Iris pattern is one of the most consistent biometric methods used for recognizing and identifying persons. Employing videos as a capturing instrument is a pretty modern style in the area of iris biometric. The use of frame by frame method provides more information and offers more suppleness compared to old-fashioned still images. Nevertheless, the size, quality and shape of the iris might differ between a frame and another. Additionally, to getting best performance it need a rapid and precise method to segment iris to amelioration rate of recognition. This work presents a method for choosing the best frames found in an iris video. This method is based on detecting motion blur and occlusion in iris videos and investigating their influence on the process of recognition. This proposed is followed by a rapid and precise method to detect pupil area, this method on the grounds of “dynamic threshold” with “Circular Hough Transform” then apply “Geodesic Active Contour” for detect outer boundary of iris. Experimental results are carried out on the MBGC NIR Iris Video datasets from the National Institute for Standards and Technology (NIST). Results show that the suggested selection method in NIR Iris Videos results in substantial enhancement in recognition efficiency. Results also indicated that the experimental evaluation of Iris segmented technique proposed in this work indicates that the precision and speed of the iris recognition via video is improved.

Original languageEnglish
Pages (from-to)294-303
Number of pages10
JournalJournal of Theoretical and Applied Information Technology
Volume94
Issue number2
Publication statusPublished - 31 Dec 2016

Fingerprint

Iris Recognition
Iris
Biometrics
Hough transforms
Motion Blur
Active Contours
Hough Transform
Occlusion
Experimental Evaluation
Geodesic
Person
Enhancement

Keywords

  • GAC iris segmentation
  • Iris biometrics
  • Non-ideal iris recognition
  • Pupil segmentation
  • Video iris recognition

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Nseaf, N. K., Shapi`i, A., Nseaf, A. K., Jaafar, A., Jassim, K. N., & Nsaif, A. K. (2016). Enhance non-ideal iris recognition system from NIR Iris video. Journal of Theoretical and Applied Information Technology, 94(2), 294-303.

Enhance non-ideal iris recognition system from NIR Iris video. / Nseaf, Nur Khder; Shapi`i, Azrulhizam; Nseaf, Asama Kuder; Jaafar, Azizah; Jassim, Khider Nassif; Nsaif, Ahmed Khudhur.

In: Journal of Theoretical and Applied Information Technology, Vol. 94, No. 2, 31.12.2016, p. 294-303.

Research output: Contribution to journalArticle

Nseaf, NK, Shapi`i, A, Nseaf, AK, Jaafar, A, Jassim, KN & Nsaif, AK 2016, 'Enhance non-ideal iris recognition system from NIR Iris video', Journal of Theoretical and Applied Information Technology, vol. 94, no. 2, pp. 294-303.
Nseaf, Nur Khder ; Shapi`i, Azrulhizam ; Nseaf, Asama Kuder ; Jaafar, Azizah ; Jassim, Khider Nassif ; Nsaif, Ahmed Khudhur. / Enhance non-ideal iris recognition system from NIR Iris video. In: Journal of Theoretical and Applied Information Technology. 2016 ; Vol. 94, No. 2. pp. 294-303.
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