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One of the most common problems encountered in human-computer interaction isautomatic facial expression recognition. Although it is easy for human observerto recognize facial expressions, automatic recognition remains difficult formachines. One of the methods that machines can recognize facial expression isanalyzing the changes in face during facial expression presentation. In thispaper, optical flow algorithm was used to extract deformation or motion vectorscreated in the face because of facial expressions. Then, these extracted motionvectors are used to be analyzed. Their positions and directions were exploitedfor automatic facial expression recognition using different data miningtechniques. It means that by employing motion vector features used as our data,facial expressions were recognized. Some of the most state-of-the-artclassification algorithms such as C5.0, CRT, QUEST, CHAID, Deep Learning (DL),SVM and Discriminant algorithms were used to classify the extracted motionvectors. Using 10-fold cross validation, their performances were calculated. Tocompare their performance more precisely, the test was repeated 50 times.Meanwhile, the deformation of face was also analyzed in this research. Forexample, what exactly happened in each part of face when a person showed fear?Experimental results on Extended Cohen-Kanade (CK+) facial expression datasetdemonstrated that the best methods were DL, SVM and C5.0, with the accuracy of95.3%, 92.8% and 90.2% respectively.

What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors