An ANN based combined classifier approach for facial emotion recognition

Official URL: http://risc01.sabanciuniv.edu/record=b1817064 (Table of Contents)

Facial expressions are the simplest reflections of human emotions, which are at the same time an integral part of any communication. Over the last decade, facial emotion recognition has attracted a great deal of research interest due to its various applications in the fields such as human computer interaction, robotics and data analytics. In this thesis, we present a facial emotion recognition approach that is based on facial expressions to classify seven emotional states: neutral, joy, sadness, surprise, anger, fear and disgust. To perform classification, two different facial features called Action Units (AUs) and Feature Point Positions (FPPs) are extracted from image sequences. A depth camera is used to capture image sequences collected from 13 volunteers to classify seven emotional states. Having extracted two sets of features, separate artificial neural network classifiers are trained. Logarithmic Opinion Pool (LOP) is then employed to combine the decision probabilities coming from each classifier. Experimental results are quite promising and establish a basis for future work on the topic.