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.

• Type: Conference
• Year: 2003
• Where: CIVR
• Authors: Ira Cohen, Nicu Sebe, Yafei Sun, Michael S. Lew, Thomas S. Huang
• Keywords: CIVR 2003, Different Facial Motion, Facial Expression, Humans Display Emotions