Paper Info
Title
A Comparative Study of Feature Extraction Approaches for an Efficient Iris Recognition System
Abstract
A wide variety of biometrics based tools are under development to meet the challenges in security in the existing complex scenario. Among these, iris pattern based identification is the most promising for its stability, reliability, uniqueness, noninvasiveness and immunity from duplication. Hence the iris identification technique has become hot research point in the past several years. This paper compares recognition rates, speed and other efficiency parameters resulting from three iris feature extraction algorithms that use statistical measures, lifting wavelet transform (LWT), and Gray-Level Co-occurrence Matrix (GLCM) respectively. Experimental results show that while LWT provides higher recognition rate, GLCM approach offers reduction in computation time with a small compromise in recognition rate. It also demonstrates that statistical measures is the most economical when recognition requirement is crucial.
Year
DOI
Venue
2010
10.1007/978-3-642-12214-9_68
Communications in Computer and Information Science
Keywords
Field
DocType
Iris Recognition,Texture analysis,Statistical Measures,Lifting Wavelet Transform,GLCM
Iris recognition,Feature extraction algorithm,Pattern recognition,Feature extraction,Artificial intelligence,Biometrics,Engineering,Computation,Wavelet transform
Conference
Volume
ISSN
Citations 
70
1865-0929
1
PageRank 
References 
Authors
0.35
2
2
Name
Order
Citations
PageRank
Chandrashekar M. Patil131.14
Sudarshan Patilkulkarni222.14