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Previous approaches to model and analyze facial expression analysis use threedifferent techniques: facial action units, geometric features and graph basedmodelling. However, previous approaches have treated these techniqueseparately. There is an interrelationship between these techniques. The facialexpression analysis is significantly improved by utilizing these mappingsbetween major geometric features involved in facial expressions and the subsetof facial action units whose presence or absence are unique to a facialexpression. This paper combines dimension reduction techniques and imageclassification with search space pruning achieved by this unique subset offacial action units to significantly prune the search space. The performanceresults on the publicly facial expression database shows an improvement inperformance by 70% over time while maintaining the emotion recognitioncorrectness.

Unifying Geometric Features and Facial Action Units for Improved Performance of Facial Expression Analysis