Facial Expression Recognition Through Machine Learning

Facial expressions communicate non-verbal cues which play an important role in interpersonal relations. Automatic recognition of facial expressions can be an important element of normal human-machine interfaces it might likewise be utilized as a part of behavioral science and in clinical practice. In spite of the fact that people perceive facial expressions for all intents and purposes immediately solid expression recognition by machine is still a challenge. From the point of view of automatic recognition a facial expression can be considered to comprise of disfigurements of the facial parts and their spatial relations or changes in the faces pigmentation. Research into automatic recognition of the facial expressions addresses the issues encompassing the representation and arrangement of static or dynamic qualities of these distortions or face pigmentation. We get results by utilizing the CVIPtools. We have taken train data set of six facial expressions of three persons and for train data set purpose we have total border mask sample 90 and 30 border mask sample for test data set purpose and we use RST- Invariant features and texture features for feature analysis and then classified them by using k- Nearest Neighbor classification algorithm. The maximum accuracy is 90.