An iris recognition method based on 2DWPCA and neural network

Author

Zhiping, Zhou ; Maomao, Hui ; Ziwen, Sun

Author_Institution

Jiangnan Univ., Wuxi, China

fYear

2009

fDate

17-19 June 2009

Firstpage

2357

Lastpage

2360

Abstract

An iris recognition method based on two-dimensional weighted principal component analysis (2DWPCA) and adaptive artificial neural network is proposed. As different iris region contains different recognition information, different weighting value is allocated to different region after compensating illumination intensity of the image in preprocessing. The two-dimensional principal component analysis is used to calculate the weighted subspace. And then 2DWPCA is utilized to extract the feature. Adaptive artificial neural network is employed to train and recognize the generated feature vectors. Owing to the 2DPCA features optimization of 2DPCA extraction and the self-adaption of neural network, the recognition ratio and robustness were greatly improved.

Keywords

biometrics (access control); feature extraction; image recognition; neural nets; principal component analysis; 2DWPCA; adaptive artificial neural network; feature extraction; iris recognition method; two-dimensional weighted principal component analysis; Adaptive systems; Artificial neural networks; Data mining; Feature extraction; Image recognition; Iris recognition; Lighting; Neural networks; Principal component analysis; Robustness; image processing; iris recognition; neural network; principal component analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2009. CCDC '09. Chinese

Conference_Location

Guilin

Print_ISBN

978-1-4244-2722-2

Electronic_ISBN

978-1-4244-2723-9

Type

conf

DOI

10.1109/CCDC.2009.5192124

Filename

5192124