Representation and classification of iris textures based on diagonal linear discriminant analysis

Author

Assunção, E.T. ; Pereira, J.R.G. ; Costa, M.G.F. ; Filho, C. F F Costa ; Padilla, R.

Author_Institution

Centro de Tecnol. Eletron. e da Informacao, UFAM, Manaus, Brazil

fYear

2011

fDate

16-17 June 2011

Firstpage

66

Lastpage

69

Abstract

Subspace methods are frequently used in pattern recognition problems aiming to reduce space dimension by determining its projection vectors. This paper presents subspace methods for feature extraction in an iris image called two-dimensional linear discriminant analysis (2DLDA), diagonal linear discriminant analysis (DiaLDA) and their combination (DiaLDA+2DLDA). The methods were applied in an UBIRIS image database, and the experimental results showed that DiaLDA+2DLDA overcame the 2DLDA method in recognition accuracy. Both methods are powerful in terms of dimension reduction and class discrimination.

Keywords

feature extraction; image classification; image representation; image texture; iris recognition; pattern recognition; 2DLDA; DiaLDA; UBIRIS image database; diagonal linear discriminant analysis; feature extraction; iris image texture; pattern recognition; projection vector; space dimension reduction; subspace method; two-dimensional linear discriminant analysis; Databases; Face; Face recognition; Feature extraction; Iris recognition; Linear discriminant analysis; dimension reduction; feature extraction; iris biometry; pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

IVMSP Workshop, 2011 IEEE 10th

Conference_Location

Ithaca, NY

Print_ISBN

978-1-4577-1284-5

Type

conf

DOI

10.1109/IVMSPW.2011.5970356

Filename

5970356