  Previous approaches to model and analyze facial expression analysis use three
different techniques: facial action units, geometric features and graph based
modelling. However, previous approaches have treated these technique
separately. There is an interrelationship between these techniques. The facial
expression analysis is significantly improved by utilizing these mappings
between major geometric features involved in facial expressions and the subset
of facial action units whose presence or absence are unique to a facial
expression. This paper combines dimension reduction techniques and image
classification with search space pruning achieved by this unique subset of
facial action units to significantly prune the search space. The performance
results on the publicly facial expression database shows an improvement in
performance by 70% over time while maintaining the emotion recognition
correctness.
