Research On Facial Expression Recognition And Affective Experience Modeling Based On Cooperative Interaction

With the rapid development of intelligent computing, humans are accustomed to solve problems with Human-Computer Interaction (HCI). It is an important issue to understand and master human's internal affective experience, because it can improve the rationality and collaboration of HCI and achieve the vivid and seamless interaction. However, considering the complexity of affective computing, we study the inner mechanism between the external facial expression and the internal affective experience based on real-time analysis of facial expressions, and achieve the meaningful results as follows:In this paper, a novel approach to facial expression analysis in parameter space with metric tensor is presented with a summary of methodologies. Based on metric tensor and its differential operators, the facial features are transformed to construct unified formalizations from the image pixel space to the parameter space with their geometric and physical characteristics preservation. Besides, three groups of experiments are conducted on standard facial expression databases and on real-time analysis to evaluate the effect of parameter space. It is suggested that the parameter space has the characteristics to lower the data formalizations demands and to improve the precision of facial expression recognition, and it can perform real-time facial expression analysis with distinction.From the view of cognitive intelligence, organizational environment and person-independent model for facial expression analysis are proposed. Since the participants are bounded by organizational norms, it enhances the operability and reliability of analysis. A person-independent model is responsible for one participant in the organizational environment, and it can improve the analytical skills through the models'cooperative interactions and the iterative transmission for the cluster structure of facial expressions. With the cooperative interactions, the person-independent model can improve the algorithm convergence faster and give the global optimal clustering distribution of facial expressions.According to person-independent model and Bayesian networks, facial expression networks are proposed to achieve the environment index factors analysis and predicting inference. Also, cooperative interactions are conducted for the structure learning of facial expression networks, and the new algorithm can improve the quality of the networks topology. Within the facial expression networks, not only can the main environmental factor causes of facial expression be analyzed, but also the model can predict what kind of facial expression would be most likely expressed next time. With such a framework of facial expression networks, the researchers can focus on the affective experiences modeling on the basis of facial expression.Based on the above studies, we design an affective experience model to analyze the coherence distribution. The model can analyze whether the participant hides its emotion or whether the facial expression and affective experience are of inner consistency. As the person-independent model can easily collect the facial features and effectively achieve the facial expression recognition and prediction, facial features and facial states are defined as affective characteristics evidences. As well as the algorithms of collaborative reliance evaluation, the model can analyze the affective experience and represent the coherence distribution diagram in space. With such a manner, the distribution is considered as the most individual intelligent ability for facial expression analysis and affective computing.In summary, this paper studies how to analyze the internal affective experience through the external facial expression based on which the affective computing can be improved and perfected. This paper also provides the basic significance for harmonious HCI.