DocumentCode :
2106425
Title :
Multi-Modal Biometrics Pixel Level Fusion and KPCA-RBF Feature Classification for Single Sample Recognition Problem
Author :
Ma, Wen-Ying ; Li, Sheng ; Yao, Yong-Fang ; Lan, Chao ; Gao, Shi-Qiang ; Tang, Hui ; Jing, Xiao-Yuan
Author_Institution :
Nanjing Univ. of Posts & Telecommun., Nanjing, China
fYear :
2009
fDate :
17-19 Oct. 2009
Firstpage :
1
Lastpage :
5
Abstract :
The single sample recognition problem is a difficult research topic in the field of biometrics, since very limited training samples and image discriminant information can be acquired. We propose a new multi-modal biometrics fusion approach to try to solve this problem, which uses face and palmprint biometrics. We combine the normalized Gaborface and Gaborpalm images in the pixel level, and present a Kernel PCA plus RBF classifier (KPRC) to classify the fused images. Testing on a large face database (Feret) and a large palmprint database, the experimental results demonstrate that the proposed pixel level fusion approach can significantly improve the recognition effects of single-modal biometrics. In addition, our approach is superior to a conventional decision level fusion method.
Keywords :
Gabor filters; biometrics (access control); face recognition; feature extraction; image classification; image recognition; Feret; Gaborface images; Gaborpalm images; KPCA-RBF feature classification; Kernel PCA; RBF classifier; face biometrics; image discriminant information; large face database; large palmprint database; multi-modal biometrics pixel level fusion; palmprint biometrics; single sample recognition problem; Biometrics; Face recognition; Feature extraction; Gabor filters; Image databases; Kernel; Neural networks; Pixel; Principal component analysis; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing, 2009. CISP '09. 2nd International Congress on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4244-4129-7
Electronic_ISBN :
978-1-4244-4131-0
Type :
conf
DOI :
10.1109/CISP.2009.5302280
Filename :
5302280
Link To Document :
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