Classification of Emotion from Facial Image Using Dimensionality Reduction Technique

Expression detection is useful as a non-invasive method of lie detection and behavior prediction. However, these facial expressions may be difficult to detect to the untrained eye. A facial expression recognition system needs to solve the following problems: detection and location of faces in a cluttered scene, facial feature extraction, and facial expression classification. Facial expression plays very important role in the communication within the human beings. It is very important to understand the presence of mind using face expression such as the situation of mind can be read by their mouth, eyes, eyebrows etc. No wonder automatic face expression recognition has become an area of immense interest within the computer science, psychology, medicine and human-computer interaction research communities. In this thesis, an investigation has been made on classification of emotion through facial images using Principal component analysis (PCA) as a dimensional reduction technique