Facial Expression Recognition using SVM Classification on Micro-Macro Patterns
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The identification of facial expressions is a fundamental topic in the area of human computer interaction and pattern recognition. The research has gained significant attention in recent years. However many challenges still exist. This is because an individual might display different expressions at different times for the same mood. Expressions can also be influenced by health. Our proposed framework aims to capture unique information related to expressions from salient patches. We extract representative feature patterns at both micro and macro levels within a pixel-patch, and use a support vector machine (SVM) classifier to label expressions. Our experimental results using the Japanese facial expression (JAFEE) and Cohn-Kanade (CK) datasets achieve high recognition rate and efficient computation time, outperforming existing work.
Citation
H. Babiker, R. Goebel, I. Cheng. "Facial Expression Recognition using SVM Classification on Micro-Macro Patterns". IEEE International Conference on Image Processing (ICIP), Beijing, China, September 2017.| Keywords: | |
| Category: | In Conference |
| Web Links: | IEEE |
BibTeX
@incollection{Babiker+al:(ICIP)17,
author = {Housam Babiker and Randy Goebel and Irene Cheng},
title = {Facial Expression Recognition using SVM Classification on
Micro-Macro Patterns},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
year = 2017,
}Last Updated: September 10, 2020Submitted by Sabina P
