Facial emotion recognition for Human-Computer Interactions using hybrid feature extraction technique

Facial expression recognition is the most important criteria for effective Human Computer Interaction (HCI) as well as a medium to understand and communicate with children who cannot emote verbally. In this paper, we propose a feature extraction technique by embedding 2D-LDA and 2D-PCA. The features extracted were then tested on standard classifiers i.e., Support Vector Machine (SVM) and K-Nearest Neighbourhood (KNN) classifiers. Facial expression images from JAFFE and Cohn-Kennedy databases were utilized for training as well as testing. Very high facial emotion recognition rate of 97.63% and 94.8% has been obtained with the proposed method for JAFFE and Cohn-Kanade databases respectively.