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Iris Recognition System based on Multi-resolution Analysis and Support Vector Machine

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Manisha Nirgude, Sachine Gengaje

Manisha Nirgude and Sachine Gengaje. Iris Recognition System based on Multi-resolution Analysis and Support Vector Machine. International Journal of Computer Applications 173(7):28-33, September 2017. BibTeX

	author = {Manisha Nirgude and Sachine Gengaje},
	title = {Iris Recognition System based on Multi-resolution Analysis and Support Vector Machine},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2017},
	volume = {173},
	number = {7},
	month = {Sep},
	year = {2017},
	issn = {0975-8887},
	pages = {28-33},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2017915366},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Iris recognition system is becoming more popular day by day and is being used in many sectors for authentication replacing traditional methods like password, ATM etc. Iris recognition system is more accurate due to unique and stable iris patterns. Here, a feature extraction method based on multi-resolution analysis is proposed. Iris image is represented at multiple resolution levels and feature vector is formed by combining detailed information obtained at different resolution levels. Further, support vector machine classifier is used for recognition purpose to handle nonlinearity of features. Experiment is performed using CASIA 3.0 database with an objective to arrive at optimum number of features with high recognition rate.


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Iris recognition, Multi-resolution analysis, wavelet transform, support vector machine, RBF kernel