Recognition of facial expressions - Knowledge Based Systems ...

These parameters are used as input to classifiers based on Bayesian Beliaf Networks,
neural networks or other classifiers to recognize upper facial action units and all their
possible combinations.
The base for the expression recognition engine is supported through a BBN model that
also handles the time behavior of the visual features.
On a completely natural dataset with lots of head movements, pose changes and
occlusions (Cohn-Kanade AU-coded facial expression database), the new probabilistic
framework (based on BBN) achieved a recognition accuracy of 68 %.
Other experiments implied the use of Linear Vector Quantization (LVQ) method,
Probabilistic Neural Networks or Back-Prop Neural Networks. The results can be seen in
the Experiments section of the report.
There are some items on the design of the project that have not been fully covered yet.
Among them, the most important is the inclusion of temporal-based parameters to be
used in the recognition process. At the moment of running the experiments there were no
data available on the dynamic behavior of the model parameters. However, the dynamic
aspects of the parameters constitutes subject to further research in the field of facial
expression recognition.
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