Research on face and iris feature recognition based on 2DDCT and Kernel Fisher Discriminant Analysis

Author

Gan, Jun-Ying ; Gao, Jian-hu ; Liu, Jun-feng

Author_Institution

Sch. of Inf., Wuyi Univ., Jiangmen

Volume

1

fYear

2008

fDate

30-31 Aug. 2008

Firstpage

401

Lastpage

405

Abstract

Combined with diagonal image transform, two-dimensional discrete cosine transform (2DDCT) is used in face and iris image for feature compression; then Kernel Fisher Discriminant Analysis (KFDA) is chosen as feature fusion; finally, Nearest Neighbor (NN) classifier is selected to perform recognition. Experimental results on ORL (Olivetti Research Laboratory) face database and CASIA (Chinese Academy of Sciences, Institute of Automation) iris database show that the dimension is reduced, the classified information is utilized, and correct recognition rate is improved effectively. A new approach is supplied for multimodal biometric identification.

Keywords

biometrics (access control); discrete cosine transforms; face recognition; feature extraction; image classification; image fusion; visual databases; 2D discrete cosine transform; 2DDCT; Chinese Academy of Sciences; Institute of Automation; Kernel fisher discriminant analysis; Olivetti Research Laboratory; diagonal image transform; face database; face recognition; feature compression; feature fusion; iris database; iris feature recognition; multimodal biometric identification; nearest neighbor classifier; Discrete cosine transforms; Discrete transforms; Face recognition; Image analysis; Image coding; Image databases; Iris; Kernel; Performance analysis; Spatial databases; Face Recognition; Feature Fusion; Iris Recognition; Two-Dimensional Discrete Cosine Transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Wavelet Analysis and Pattern Recognition, 2008. ICWAPR '08. International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4244-2238-8

Electronic_ISBN

978-1-4244-2239-5

Type

conf

DOI

10.1109/ICWAPR.2008.4635812

Filename

4635812