Title
An experimental evaluation of linear and kernel-based classifiers for face recognition
Abstract
This paper presents the results of a comparative study of linear and kernel-based methods for face recognition. We focus mainly on the experimental comparison of classification methods, i.e. Nearest Neighbor, Linear Support Vector Machine, Kernel based Nearest Neighbor and Nonlinear Support Vector Machine. Some interesting conclusions can be obtained after all of these methods are performed on two wellknown database, i.e. ORL, YALE Face Database, respectively.
Year
DOI
Venue
2005
10.1007/11427445_21
ISNN (2)
Keywords
Field
DocType
comparative study,interesting conclusion,face recognition,experimental evaluation,yale face database,classification method,experimental comparison,nearest neighbor,kernel-based classifier,vector machine,nonlinear support,linear support,support vector machine
k-nearest neighbors algorithm,Kernel (linear algebra),Facial recognition system,Nearest neighbour,Nonlinear system,Pattern recognition,Computer science,Support vector machine,Kernel principal component analysis,Artificial intelligence,Kernel method,Machine learning
Conference
Volume
ISSN
ISBN
3497
0302-9743
3-540-25913-9
Citations 
PageRank 
References 
0
0.34
7
Authors
4
Name
Order
Citations
PageRank
Congde Lu1142.72
Taiyi Zhang217617.60
Wei Zhang322619.22
Guang Yang410.83