Facial Expression Monitoring System Using PCA-Bayes Classifier

In order to endow a machine with an emotional intelligence is a challenging research issue and become one that has been of growing importance to those working in human-computer interaction. This study presents the framework of a special session to study and investigate the best techniques for emotion recognition, validation and analysis of expressivity in human-computer interaction, based on the common physiological background. A PCA-Bayes classifier (PCABC) was proposed in this study for facial recognition problem. The session is primarily concerned with visual emotion analysis; the analysis of physiological signals serves as a complement to this modality. Signal is taken from a different aspect of the physiology and visual. The signal will go through a process of elimination votes in order to extract better signal features. It is shown that the PCABC can perform much better than Least Mean Square (LMS) classifier. Psychological backgrounds will be studied to obtain good signal.