Facial expression recognition by analyzing features of conceptual regions

Facial expression recognition utilizes collection of information from characteristic actions to analyze emotions and mental states of a person. It has emerged as the pivotal research topics in areas such as human computer interaction, sentimental analysis and synthetic face animation over the last years. This paper proposes an approach for facial expression by discovering associations between visual feature and Local Binary Pattern (LBP). Unlike many previous studies, the proposed approach automatically tracks the facial area and segments face into meaningful areas based on description of Local Binary Pattern. And then it accumulates the probabilities throughout the frames from video data to capture the temporal characteristics of facial expressions by analyzing facial expressions. Through the proposed approach, the temporal variation of facial expression can be quantified in individual areas. Thus, the recognition process of facial expression tends to be more comprehensible without sacrificing results of recognition. The empirical evaluation results of the approach are realized using video data which is collected from 10 volunteers. The results demonstrated that the proposed approach can effectively segment face into specific area and recognize facial expression.