Iris feature extraction using optimized Gabor wavelet based on multi objective genetic algorithm

Author

Ghodrati, Hamed ; Dehghani, Mohammad Javad ; Danyali, Habibolah

Author_Institution

Dept. of Telecommun. Eng., Shiraz Univ. of Technol., Shiraz, Iran

fYear

2011

fDate

15-18 June 2011

Firstpage

159

Lastpage

163

Abstract

Iris reputes for its potential to identify the people with high accuracy in large scale. This is not achieved unless the iris patterns are well represented. Gabor filtering is vastly used in iris recognition literature for feature extraction. Conventionally, Gabor parameters value are supplied by pre-knowledgeable values so that the filter bank size is increased to prevent the losing information. In this paper, multi objective genetic algorithm (MOGA) is used to optimize the Gabor-wavelet in order to reduce the filter requirements and increasing the accuracy. The feature vectors are encoded by phase quantization and a novel method based on iris texture variation. Experimental results show recognizing with CRR=99.68% and EER=0.26% for codes with length only 496 bits on a subset including 2125 iris images from CASIA-IrisV3-Interval database.

Keywords

filtering theory; genetic algorithms; image texture; iris recognition; quantisation (signal); wavelet transforms; Gabor filtering; feature vector encoding; iris feature extraction; iris recognition; iris texture variation; multiobjective genetic algorithm; optimized Gabor wavelet; phase quantization; Encoding; Feature extraction; Filter banks; Gabor filters; Iris; Iris recognition; Quantization; Gabor filter; genetic algorithm; iris recognition; optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium on

Conference_Location

Istanbul

Print_ISBN

978-1-61284-919-5

Type

conf

DOI

10.1109/INISTA.2011.5946089

Filename

5946089