A wavelet-based feature extraction technique for partial iris recognition

Md. Rabiul, Islam (2011) A wavelet-based feature extraction technique for partial iris recognition. Masters thesis, Universiti Malaysia Sarawak (UNIMAS).

[img] PDF (Please get the password by email to repository@unimas.my , or call ext: 082-583914/3973/3933)
Md. Rabiul Islam.pdf
Restricted to Registered users only

Download (10MB)

Abstract

Feature extraction is an important task in the overall processing of iris biometric in an iris based biometric authentication system. The existing approaches are using complete iris image to extract iris features. But, it is difficult to get complete iris image for feature extraction because a person's eyes are naturally covered by the eyelids and eyelashes. Thus, the correct recognition rate decreased in the existing approaches due to occluded eyelids and eyelashes classified as an iris region. Therefore, this research proposed an iris image model and a feature extraction method for partial iris recognition. Several issues that are investigated in this research i.e. which parts or regions of the iris are more stable and provide distictive texture patterns, what is the optimum coverage area of the iris that is required to extract discriminant texture features for partial iris recognition, the existing feature extraction methods and the nature of features whether they can be used for partial iris recognition.

Item Type: Thesis (Masters)
Additional Information: Thesis (M.Sc.) -- Universiti Malaysia Sarawak, 2011.
Uncontrolled Keywords: iris recognition, Universiti Malaysia Sarawak, UNIMAS , Faculty of computer science and information technology, IPTA, education, sarawak, kuching, malaysia, samarahan, universiti, university
Subjects: Q Science > QA Mathematics
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Faculties, Institutes, Centres > Faculty of Computer Science and Information Technology
Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Karen Kornalius
Date Deposited: 15 Apr 2014 02:14
Last Modified: 14 Nov 2023 01:41
URI: http://ir.unimas.my/id/eprint/1716

Actions (For repository members only: login required)

View Item View Item