Effect of supervised region of interest against edge detection method for iris localisation

Zuraini Othman, Azizi Abdullah, Fauziah Kasmin, Sharifah Sakinah Syed Ahmad

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

With the recent developments in information technology, health diagnosis based on iris analysis and biometrics has received considerable attention. For iris recognition, iris localisation, which is not an easy task, is an important phase. Moreover, for iris localisation, dealing with nonideal iris images could cause an incorrect location. Conventional methods for iris location involve multiple searches, which can be noisy and outdated. Such techniques could be inaccurate while describing pupillary boundaries and could lead to multiple errors while performing feature recognition and extraction. Hence, to address such issues, we propose a method for iris localisation of both ideal and nonideal iris images. In this research, the algorithm operates by determining all regions of interest (ROI) classifications through the use of a support vector machine (SVM) as well as the application of histograms that use grey levels as descriptors in all regions from those exhibiting growth. The valid region of interest (ROI) obtained from the probabilities graph of an SVM was obtained by examining the global minimum conditions determined using a second derivative model of the graph of functions. Moreover, this helped to eliminate the sensitive noises and decrease the calculations while reserving relevant information as far as possible. During the experiment, the comparison edge detection method was used with Canny and a multi-resolution local approach. The results demonstrated that the proposed ROI provided better results compared with those obtained without ROI.

Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
PublisherSpringer
Pages249-263
Number of pages15
DOIs
Publication statusPublished - 1 Jan 2019

Publication series

NameLecture Notes in Networks and Systems
Volume67
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Fingerprint

Edge detection
Support vector machines
Biometrics
Information technology
Health
Derivatives
Experiments

Keywords

  • Edge detection
  • Multi-resolution level
  • Region growing
  • Region of interest
  • Support vector machine

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

Cite this

Othman, Z., Abdullah, A., Kasmin, F., & Ahmad, S. S. S. (2019). Effect of supervised region of interest against edge detection method for iris localisation. In Lecture Notes in Networks and Systems (pp. 249-263). (Lecture Notes in Networks and Systems; Vol. 67). Springer. https://doi.org/10.1007/978-981-13-6031-2_44

Effect of supervised region of interest against edge detection method for iris localisation. / Othman, Zuraini; Abdullah, Azizi; Kasmin, Fauziah; Ahmad, Sharifah Sakinah Syed.

Lecture Notes in Networks and Systems. Springer, 2019. p. 249-263 (Lecture Notes in Networks and Systems; Vol. 67).

Research output: Chapter in Book/Report/Conference proceedingChapter

Othman, Z, Abdullah, A, Kasmin, F & Ahmad, SSS 2019, Effect of supervised region of interest against edge detection method for iris localisation. in Lecture Notes in Networks and Systems. Lecture Notes in Networks and Systems, vol. 67, Springer, pp. 249-263. https://doi.org/10.1007/978-981-13-6031-2_44
Othman Z, Abdullah A, Kasmin F, Ahmad SSS. Effect of supervised region of interest against edge detection method for iris localisation. In Lecture Notes in Networks and Systems. Springer. 2019. p. 249-263. (Lecture Notes in Networks and Systems). https://doi.org/10.1007/978-981-13-6031-2_44
Othman, Zuraini ; Abdullah, Azizi ; Kasmin, Fauziah ; Ahmad, Sharifah Sakinah Syed. / Effect of supervised region of interest against edge detection method for iris localisation. Lecture Notes in Networks and Systems. Springer, 2019. pp. 249-263 (Lecture Notes in Networks and Systems).
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