원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.4 No.1
2011.01
pp.25-32
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
This paper deals with Face recognition using Single training sample which is a new challenging problem in machine vision. In the proposed method, first four different representation of face are generated using Gabor filters which vary in angle. Then a Baseclassifier is assigned for each of them and also for original image. Finally EMV technique combines the Base-classifiers. EMV behaves like MV but chooses the vote of the Baseclassifier assigned to original image as winner class when there is multiple winner class. Experimental results on ORL face dataset, show an improvement about 2%, 4% and 5% than 2DPCA, (PC)2A and PCA respectively.
목차
Abstract
1. Introduction
2. Outline of Two Dimensional Principle Component Analysis
2.1 Basic steps of 2DPCA
3. Outline of Gabor filter
4. Proposed method
4.1 Applying a set of Gabor filters on face images to create a new representation of them
4.2 Extracting the informative features from samples using 2DPCA transform
4.3 Classifying the face images using a two-stage classifier system
5. Experimental results
6. Conclusion
References
1. Introduction
2. Outline of Two Dimensional Principle Component Analysis
2.1 Basic steps of 2DPCA
3. Outline of Gabor filter
4. Proposed method
4.1 Applying a set of Gabor filters on face images to create a new representation of them
4.2 Extracting the informative features from samples using 2DPCA transform
4.3 Classifying the face images using a two-stage classifier system
5. Experimental results
6. Conclusion
References
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자료제공 : 네이버학술정보