Facial Expression Recognition using Conspicuous Features Selection and Comparison of the Performance of Different Classifiers

In this work we develop a fast facial expression recognition system based on cross correlation with low complexity by proposing a method that does not need face detection for facial points tracking. Moreover, our simple feature selection according to the facial characteristics differentiates between the six basic expressions (happiness, surprise, sadness, disgust, fear and anger). In this system, 20 selected facial feature points from the first frame to the last are tracked automatically using a cross-correlation optical flow. The extracted feature vector is then given to following classifiers: Bayes optimal classifier with two approaches in probability density function estimation, K-nearest neighbor and support vector machine with radial basis function kernel. These classifiers are analyzed according to their correct classification rate by the cross validation method. For Cohn-Kanade database the best result is obtained by Bayes optimal classifier with the average correct classification rate (Ave-CCR) of 89.67%.