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IRIS Recognition using Texture Features Extracted from Haarlet Pyramid

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International Journal of Computer Applications
© 2010 by IJCA Journal
Number 12 - Article 1
Year of Publication: 2010
Authors:
Dr.H.B.Kekre
Sudeep D. Thepade
Juhi Jain
Naman Agrawal
10.5120/1638-2202

Dr.H.B.Kekre, Sudeep D Thepade, Juhi Jain and Naman Agrawal. Article:IRIS Recognition using Texture Features Extracted from Haarlet Pyramid. International Journal of Computer Applications 11(12):1–5, December 2010. Published By Foundation of Computer Science. BibTeX

@article{key:article,
	author = {Dr.H.B.Kekre and Sudeep D. Thepade and Juhi Jain and Naman Agrawal},
	title = {Article:IRIS Recognition using Texture Features Extracted from Haarlet Pyramid},
	journal = {International Journal of Computer Applications},
	year = {2010},
	volume = {11},
	number = {12},
	pages = {1--5},
	month = {December},
	note = {Published By Foundation of Computer Science}
}

Abstract

Iris recognition has been a fast growing, challenging and interesting area in real-time applications. A large number of iris recognition algorithms have been developed for decades. The paper presents novel Haarlet Pyramid based iris recognition technique. Here iris recognition is done using the image feature set extracted from Haar Wavelets at various levels of decomposition. Analysis was performed of the proposed method, consisting of the False Acceptance Rate and the Genuine Acceptance Rate. The proposed technique is tested on an iris image database having 384 images. The results show that Haarlets level-5 outperforms other Haarlets, because the higher level Haarlets are giving very fine texture features while the lower level Haarlets are representing very coarse texture features which are less useful for discrimination of images in iris recognition.

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