Open AccessEfficient Iris Recognition through Improvement of Feature Vector and Classifier
Author(s) -
Lim Shinyoung,
Lee Kwanyong,
Byeon Okhwan,
Kim Taiyun
Publication year - 2001
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.01.0101.0203
Subject(s) - artificial intelligence , initialization , optimal distinctiveness theory , computer science , pattern recognition (psychology) , iris recognition , feature vector , classifier (uml) , wavelet transform , feature selection , identification (biology) , biometrics , machine learning , wavelet , psychology , botany , psychotherapist , biology , programming language
In this paper, we propose an efficient method for personal identification by analyzing iris patterns that have a high level of stability and distinctiveness. To improve the efficiency and accuracy of the proposed system, we present a new approach to making a feature vector compact and efficient by using wavelet transform, and two straightforward but efficient mechanisms for a competitive learning method such as a weight vector initialization and the winner selection. With all of these novel mechanisms, the experimental results showed that the proposed system could be used for personal identification in an efficient and effective manner.