  One of the most common problems encountered in human-computer interaction is
automatic facial expression recognition. Although it is easy for human observer
to recognize facial expressions, automatic recognition remains difficult for
machines. One of the methods that machines can recognize facial expression is
analyzing the changes in face during facial expression presentation. In this
paper, optical flow algorithm was used to extract deformation or motion vectors
created in the face because of facial expressions. Then, these extracted motion
vectors are used to be analyzed. Their positions and directions were exploited
for automatic facial expression recognition using different data mining
techniques. It means that by employing motion vector features used as our data,
facial expressions were recognized. Some of the most state-of-the-art
classification algorithms such as C5.0, CRT, QUEST, CHAID, Deep Learning (DL),
SVM and Discriminant algorithms were used to classify the extracted motion
vectors. Using 10-fold cross validation, their performances were calculated. To
compare their performance more precisely, the test was repeated 50 times.
Meanwhile, the deformation of face was also analyzed in this research. For
example, what exactly happened in each part of face when a person showed fear?
Experimental results on Extended Cohen-Kanade (CK+) facial expression dataset
demonstrated that the best methods were DL, SVM and C5.0, with the accuracy of
95.3%, 92.8% and 90.2% respectively.
