Abstract
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.
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Cohen, I., Sebe, N., Sun, Y., Lew, M.S., Huang, T.S. (2003). Evaluation of Expression Recognition Techniques. In: Bakker, E.M., Lew, M.S., Huang, T.S., Sebe, N., Zhou, X.S. (eds) Image and Video Retrieval. CIVR 2003. Lecture Notes in Computer Science, vol 2728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45113-7_19
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DOI: https://doi.org/10.1007/3-540-45113-7_19
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