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CIVR
2003
Springer

Evaluation of Expression Recognition Techniques

9 years 8 months ago
Evaluation of Expression Recognition Techniques
The most expressive way humans display emotions is through facial expressions. In this work we report on several advances we have made in building a system for classification of facial expressions from continuous video input. We introduce and test different Bayesian network classifiers for classifying expressions from video. In particular we use Naive-Bayes classifiers and to learn the dependencies among different facial motion features we use Tree-Augmented Naive Bayes (TAN) classifiers. We also investigate a neural network approach. Further, we propose an architecture of hidden Markov models (HMMs) for automatically segmenting and recognizing human facial expression from video sequences. We explore both person-dependent and person-independent recognition of expressions and compare the different methods.
Ira Cohen, Nicu Sebe, Yafei Sun, Michael S. Lew, T
Added 06 Jul 2010
Updated 06 Jul 2010
Type Conference
Year 2003
Where CIVR
Authors Ira Cohen, Nicu Sebe, Yafei Sun, Michael S. Lew, Thomas S. Huang
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