DocumentCode :
2319739
Title :
Coupling Adaboost and Random Subspace for Diversified Fisher Linear Discriminant
Author :
Kong, Hui ; Teoh, Eam Khwang
Author_Institution :
Digital Video Office, Panasonic Singapore Lab
fYear :
2006
fDate :
5-8 Dec. 2006
Firstpage :
1
Lastpage :
5
Abstract :
Fisher linear discriminant (FLD) is a popular method for feature extraction in face recognition. However, It often suffers from the small sample size, bias and overfitting problems when dealing with the high dimensional face image data. In this paper, a framework of ensemble learning for diversified Fisher linear discriminant (EnL - DFLD) is proposed to improve the current FLD based face recognition algorithms. Firstly, the classifier ensemble in EnL - DFLD is composed of a set of diversified component FLD classifiers, which are selected intentionally by computing the diversity between the candidate component classifiers. Secondly, the candidate component classifiers are constructed by coupling the random subspace and adaboost methods, and it can also be shown that such a coupling scheme will result in more suitable component classifiers so as to increase the generalization performance of EnL - DFLD. Experiments on two common face databases verify the superiority of the proposed EnL - DFLD over the state-of-the-art algorithms in recognition accuracy
Keywords :
face recognition; feature extraction; generalisation (artificial intelligence); image classification; learning (artificial intelligence); random processes; adaboost method; candidate component classifier; diversified Fisher linear discriminant; ensemble learning; face image; face recognition; feature extraction; generalization; random subspace; Bagging; Diversity reception; Ear; Face recognition; Feature extraction; Image databases; Iris; Kernel; Machine learning; Speech; Ensemble learning; Face; LDA;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control, Automation, Robotics and Vision, 2006. ICARCV '06. 9th International Conference on
Conference_Location :
Singapore
Print_ISBN :
1-4244-0341-3
Electronic_ISBN :
1-4214-042-1
Type :
conf
DOI :
10.1109/ICARCV.2006.345426
Filename :
4150230
Link To Document :
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