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.
Department(s)
Information Technology and Cybersecurity
Document Type
Conference Proceeding
DOI
https://doi.org/10.1007/11676935_15
Publication Date
6-23-2006
Recommended Citation
Brahnam, Sheryl, Chao-Fa Chuang, Frank Y. Shih, and Melinda R. Slack. "SVM classification of neonatal facial images of pain." In International Workshop on Fuzzy Logic and Applications, pp. 121-128. Springer, Berlin, Heidelberg, 2005.
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
