A Facial Expression Recognition Model using Support Vector Machines

Facial Expression Recognition (FER) has gained interest among researchers due to its inevitable role in the human computer interaction. In this paper, an FER model is proposed using principal component analysis (PCA) as the dimensionality reduction technique, Gabor wavelets and Local binary pattern (LBP) as the feature extraction techniques and support vector machine (SVM) as the classification technique. The experimentation was done on Cohn-Kanade, JAFFE, MMI Facial Expression datasets and real time facial expressions using a webcam. The proposed methods outperform the existing methods surveyed.