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Applying Facial Action Units and Feature Selection Methods to Develop the Learning Emotion Image Database and Recognition Model
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Emotion is the psychological state that human are exposed to various events. The psychological state is closely related to the physical state and it is a variety of physiological signals. Researchers judge human mental states by capturing and analyzing physiological signals. The classification of emotions is quite diverse. From basic emotions that are inborn and universally applicable to the acquisition of highly interactive and complex emotions from various social situations in the acquired growth, scholars are involved in research.
In various contexts, learning-related emotions are more valued. Emotions affect student learning, and learning effectiveness can also reflect student emotions. We focuses on the latter. Learning emotions is to explore the emotional state of students in the learning process. Researchers will start with basic emotions when discussing issues related to learning emotions, because basic emotions are easily defined by intuition. Ekman analyzes the movement of facial muscles when humans express basic emotions around the world as an action unit and explains its connection with basic emotions.
After learning the basic emotions through the action unit coding system, the researchers wanted to analyze complex learning emotions through action units. However, we found that different studies have different action units for learning emotions. Therefore, we decided to find out the correlation between learning emotions and action units. First, we will establish a database named as 'Learning Emotion Image Database' and operational definition. Labeling learning emotions and action units according to the operational definition as the training data. Then, three feature selection methods are used: decision tree, GA+SVM and ReliefF algorithm to find out the relationship between action units and learning emotions. Under the binary classification, we research the correlation between individual emotions and action units. The results showed the common AU combination of the three feature selection methods. Compared with different studies, although single action units have significant correlation with learning emotions, AU combinations can more accurately identify learning emotions. Under the multiclass classification, we research the accuracy of learning emotion recognition. The results showed that with the same accuracy, GA+SVM will have the smallest AU combination. The decision tree grabs more AU and speculates that there may be overfitting. The AU combination generated by ReliefF algorithm is different from both of them. Because the ReliefF algorithm does not consider the correlation between features and features, only the statistical correlation between the features and target categories is calculated. It cannot effectively remove redundant features.
In order to apply to the actual teaching scene, we establishes the action unit recognition model to achieve the instant recognition of learning emotion. The action unit recognition model which apply random forest classification algorithm uses feature values as input vectors to predict action units. After training and testing with Learning Emotion Image Database and CK+ database, the results show that 15 action units we used in the learning emotion model, most of them have good generalization ability and the model has good discrimination. A few action units have poor generalization ability. Finally, the action unit identification model is used to obtain the action unit, and the learning emotion recognition model is used to identify the learning emotion generated by the learner in the learning process, so as to instantly recognize learning emotion.
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