Texture classification of the human iris using artificial neural networks

Author

Alim, Onsy Abdel ; Sharkas, Maha

Author_Institution

Fac. of Eng., Alexandria Univ., Egypt

fYear

2002

fDate

2002

Firstpage

580

Lastpage

583

Abstract

Automatic personal identification systems have assumed great importance in the past few years. The iris of the human eye has a texture that is unique for each individual and remains stable over the years. In this paper two feature extraction techniques that are based on 2D Gabor wavelets and 2D DCT are suggested and compared with each other. The features obtained are fed to neural network classifiers for identification. The achieved recognition rate using the DCT coefficients was about 96% compared to 92% obtained using the Gabor coefficients.

Keywords

biometrics (access control); discrete cosine transforms; eye; feature extraction; image classification; image texture; neural nets; statistical analysis; wavelet transforms; 2D DCT; 2D Gabor wavelets; ANN; artificial neural networks; automatic personal identification systems; feature extraction; histogram; human eye; iris texture; neural network classifiers; recognition rate; texture classification; Artificial neural networks; Biomedical optical imaging; Discrete cosine transforms; Electronic mail; Fingers; Gabor filters; Humans; Iris; Optical filters; Spatial resolution;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrotechnical Conference, 2002. MELECON 2002. 11th Mediterranean

Print_ISBN

0-7803-7527-0

Type

conf

DOI

10.1109/MELECON.2002.1014659

Filename

1014659