Title
SVM classification of neonatal facial images of pain
Abstract
This paper reports experiments that explore performance differences in two previous studies that investigated SVM classification of neonatal pain expressions using the Infant COPE database. This database contains 204 photographs of 26 neonates (age 18-36 hours) experiencing the pain of heel lancing and three nonpain stressors. In our first study, we reported experiments where representative expressions of all subjects were included in the training and testing sets, an experimental protocol suitable for intensive care situations. A second study used an experimental protocol more suitable for short-term stays: the SVMs were trained on one sample and then evaluated on an unknown sample. Whereas SVM with polynomial kernel of degree 3 obtained the best classification score (88.00%) using the first evaluation protocol, SVM with a linear kernel obtained the best classification score (82.35%) using the second protocol. However, experiments reported here indicate no significant difference in performance between linear and nonlinear kernels.
Year
DOI
Venue
2005
10.1007/11676935_15
WILF
Keywords
Field
DocType
infant cope database,neonatal pain expression,neonatal facial image,best classification score,performance difference,experimental protocol,nonlinear kernel,linear kernel,polynomial kernel,evaluation protocol,svm classification
Kernel (linear algebra),Pattern recognition,Expression (mathematics),Computer science,Support vector machine,Polynomial kernel,Artificial intelligence,Intensive care,Machine learning
Conference
Volume
ISSN
ISBN
3849
0302-9743
3-540-32529-8
Citations 
PageRank 
References 
8
0.57
4
Authors
4
Name
Order
Citations
PageRank
Sheryl Brahnam161739.34
Chao-fa Chuang221810.82
Frank Y. Shih3110389.56
Melinda R. Slack4452.67