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Fully automated recognition of spontaneous facial expressions in videos using random forest classifiers

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2 Author(s)
Abd El Meguid, M.K. ; Visual Surveillance Group, McGill Univ., Montreal, QC, Canada ; Levine, M.D.

This paper discusses the design and implementation of a fully automated comprehensive facial expression detection and classification framework. It uses a proprietary face detector (PittPatt) and a novel classifier consisting of a set of Random Forests paired with support vector machine labellers. The system performs at real-time rates under imaging conditions, with no intermediate human intervention. The acted still-image Binghamton University 3D Facial Expression database was used for training purposes, while a number of spontaneous expression-labelled video databases were used for testing. Quantitative evidence for qualitative and intuitive facial expression recognition constitutes the main theoretical contribution to the field.

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Affective Computing, IEEE Transactions on  (Volume:5 ,  Issue: 2 )