Fully automated recognition of spontaneous facial expressions in videos using random forest classifiers
Issue No. 02 - April-June (2014 vol. 5)
Mostafa K. Abd El Meguid , Visual Surveillance Group, McGill Univ., Montreal, QC, Canada
Martin D. Levine , Visual Surveillance Group, McGill Univ., Montreal, QC, Canada
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.
Face, Databases, Training, Videos, Support vector machines, Radio frequency, Face recognition
M. K. Abd El Meguid and M. D. Levine, "Fully automated recognition of spontaneous facial expressions in videos using random forest classifiers," in IEEE Transactions on Affective Computing, vol. 5, no. 2, pp. , 2014.