Abstract
Facial expression recognition has been one of the most challenging researches in computer vision. Second to verbal language, facial expression is another way of body language communication. The technical difficulty of facial expression recognition lies in that every individual has a unique way of facial expression for his emotion. Even for the same person, there exists a very slight difference of facial expression for the same emotion. Facial expression results from a continuous series of physical changes of the facial muscle. These facial muscle changes are usually divided into four phases: Neutral, Onset, Apex, Offset, and Neutral. In this thesis, we apply image processing technique to recognize the Apex phases of human faces. We use Gabor filter to obtain the facial features, and then identify several minute features on the face. These detailed features serve as Action Units (AUs), of which their different combinations represent different expression. To recognize different facial expression, we find different combinations of AUs through Random Forest training to classify six typical facial expressions: anger, disgust, fear, happiness, sadness, and surprise.