Facial expression recognition using constrained local models and Hidden Markov models with consciousness-based architecture

Many different types of robots have been developed to facilitate our lives, such as those used in industrial production. However, most robots operate by following human instructions or programs rather than by acting naturally that the action are not behave by robot learning. Conversely, these actions be convinced by the human. Thus, the present research was undertaken as a step toward achieving a natural robot action through consciousness-based architecture (CBA). Our CBA system imitates animal consciousness. Here we present the implementation of a facial expression recognition system that uses constrained local models (CLMs) to fit facial features together with hidden Markov models (HMMs) to classify and recognize emotions. We propose an approach and present our CLM experimental results including time efficiency and accuracy together with the experimental results of emotion recognition such as time efficiency and a confusion matrix. The present experiment demonstrates that our proposed system is an efficient personal facial expression recognition method with the result of the recognition correctness as 96.43 percent.