Fusion and recognition of face and iris feature based on wavelet feature and KFDA

Author

Gan, Jun-Ying ; Liu, Jun-feng

Author_Institution

Sch. of Inf., Wuyi Univ., Jiangmen, China

fYear

2009

fDate

12-15 July 2009

Firstpage

47

Lastpage

50

Abstract

In this paper, a novel approach to the fusion and recognition of face and iris image based on wavelet features and kernel Fisher discriminant analysis (KFDA) is developed. Firstly, the dimension is reduced, the noise is eliminated, the storage space is saved and the efficiency is improved by discrete wavelet transform (DWT) to face and iris image. Secondly, face and iris features are extracted and fusion by KFDA. Finally, nearest neighbor classifier is selected to perform recognition. Experimental results on ORL face database and CASIA iris database show that not only the dasiasmall sample problempsila is overcome by KFDA, but also the correct recognition rate is higher than that of face recognition and iris recognition.

Keywords

discrete wavelet transforms; face recognition; feature extraction; image fusion; discrete wavelet transform; face recognition; feature extraction; feature fusion; iris recognition; kernel Fisher discriminant analysis; nearest neighbor classifier; wavelet feature; Discrete wavelet transforms; Face recognition; Image analysis; Image databases; Image recognition; Image storage; Iris; Kernel; Spatial databases; Wavelet analysis; Discrete Wavelet Transform; Face Recognition; Feature Fusion; Iris Recognition; Kernel Fisher Discriminant Analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Wavelet Analysis and Pattern Recognition, 2009. ICWAPR 2009. International Conference on

Conference_Location

Baoding

Print_ISBN

978-1-4244-3728-3

Electronic_ISBN

978-1-4244-3729-0

Type

conf

DOI

10.1109/ICWAPR.2009.5207475

Filename

5207475