Paper Info
Title
An geometrically intuitive marginal discriminant analysis method with application to face recognition
Abstract
This paper presents a new nonparametric linear feature extraction method coined geometrically intuitive marginal discriminant analysis (IMDA). Motivated by the law of cosines in trigonometry, we characterize the square local margin by a weighted difference of the square between-class distance and the square within-class distance. Based on this characterization, we design a class margin criterion which is used to determine an optimal transform matrix such that the class margin is maximized in the transformed space. The proposed method was applied to face recognition and evaluated on the Yale and the FERET databases. Experimental results demonstrate the effectiveness of the proposed method.
Year
DOI
Venue
2009
10.1109/ICIP.2009.5413958
ICIP
Keywords
Field
DocType
feret database,square between- class distance,extraction method,geometrically intuitive marginal discriminant analysis method,face recognition,discriminant analysis,feret databases,visual databases,geometrically intuitive marginal discriminant,yale database,nonparametric linear feature extraction method,dimensionality reduction,optimal transform matrix,class margin criterion,feature extraction,square between-class distance,analysis method,square within-class distance,class margin,square local margin,trigonometry,databases,image recognition,principal component analysis
k-nearest neighbors algorithm,Optimal discriminant analysis,Facial recognition system,Dimensionality reduction,Pattern recognition,Feature extraction,Law of cosines,Artificial intelligence,Linear discriminant analysis,Principal component analysis,Mathematics
Conference
ISSN
ISBN
Citations 
1522-4880 E-ISBN : 978-1-4244-5655-0
978-1-4244-5655-0
0
PageRank 
References 
Authors
0.34
7
3
Name
Order
Citations
PageRank
Jian Yang16102339.77
Zhenghong Gu2423.14
Jing-yu Yang36061345.83