DocumentCode :
3298479
Title :
Pairwise face recognition
Author :
Guo, Guo-Dong ; Zhang, Hong-Jiang ; Li, Stan Z.
Author_Institution :
Sigma Center, Microsoft Res. China, Beijing, China
Volume :
2
fYear :
2001
fDate :
2001
Firstpage :
282
Abstract :
We develop a pairwise classification framework for face recognition, in which a C class face recognition problem is divided into a set of C(C-1)/2 two class problems. Such a problem decomposition not only leads to a set of simpler classification problems to be solved, thereby increasing overall classification accuracy, but also provides a framework for independent feature selection for each pair of classes. A simple feature ranking strategy is used to select a small subset of the features for each pair of classes. Furthermore, we evaluate two classification methods under the pairwise comparison framework: the Bayes classifier and the AdaBoost. Experiments on a large face database with 1079 face images of 137 individuals indicate that 20 features are enough to achieve a relatively high recognition accuracy, which demonstrates the effectiveness of the pairwise recognition framework
Keywords :
Bayes methods; face recognition; feature extraction; image classification; AdaBoost; Bayes classifier; classification methods; classification problems; feature ranking strategy; feature selection; large face database; pairwise classification framework; pairwise comparison framework; pairwise face recognition; pairwise recognition framework; Authentication; Face recognition; Image databases; Image recognition; Pattern recognition; Principal component analysis; Robustness; Scattering; Spatial databases; Surveillance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7695-1143-0
Type :
conf
DOI :
10.1109/ICCV.2001.937637
Filename :
937637
Link To Document :
بازگشت