Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
3 Author(s)
Jun-Ying Gan ; Sch. of Inf., Wuyi Univ., Jiangmen ; Jian-Hu Gao ; Jun-Feng Liu

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.

Published in:

Wavelet Analysis and Pattern Recognition, 2008. ICWAPR '08. International Conference on  (Volume:1 )

Date of Conference:

30-31 Aug. 2008