Classroom Teaching Feedback System Based on Emotion Detection

Currently, the one-to-many teaching method is still adopted among most of the teaching classroom in modern universities. Limited by the resources of teachers, the number of students in some basic courses could be well into the hundreds. In this teaching situation, it is difficult for teachers to notice learning feedback from all student immediately. This paper proposed a real-time feedback system based on the array camera to obtain facial expressions of all students and judge the teaching effect of the knowledge points. This system can help the teacher handle the learning state of the students dynamically, so that the teaching methods or progress can be adjusted to achieve better teaching effect. Introduction Cognitive psychology shows that cognitive processing and emotion processing are integrated at multiple levels in brain [1]. For example, some cortical structures (i.e. orbitofrontal cortex) integrate cognitive and emotion information in learning progress by interacting with neural structures of emotional processing (i.e. amygdala) [2]. Drugs can reduce or increase the destructive effect of anxiety on learning[3]. Animal experiment have shown that the learning of the mouse will be affected if the amygdala processing the original neutral sensory signal into an obvious emotional signal with disgusting information [4]. The important role of emotional experience in learning enlightens us that: students can learn and understand more effectively with a classroom atmosphere which can induce positive emotions. To better induce students’ positive emotions, teachers might have to grasp the students’ learning states accurately. However, in modern classroom, especially the basic courses, students are often overcrowded, that make it difficult for teachers to monitor students’ emotion in learning comprehensively. Nevertheless, with the development of computer vision in recent years, the accuracy of facial expression based on face detection has been continuously improved. Thus, it provides a powerful guarantee for us to build a real-time feedback system of students’ classroom learning emotion. Based on the emotional classify of learning emotions, this paper uses camera array to construct image acquisition system; dynamic recognize face information in real time; uses feature extraction and emotion classifier to recognize emotion; summarize identification information to give statistical result and present to teacher in real time. Emotion and Classify A general definition of emotion is: emotion is a complex state of feeling that results in physical and psychological and influence mind and body. These changes can also affect thought and behaviors. There are also various types of emotions. Paul Ekman firstly divided emotions into six main categories: Anger, Disgust, Fear, Happy, Sadness, Surprise [5]. He also expanded other emotions such as Shy, Satisfied, Pride, Pleasure, etc. [6] Robert Plutchik [7] summarized eight different emotions and their relationship through wheel of emotion. He Wei [8] established a learner emotion model based on three-dimensional emotion model, and quantified the six explicit expressions. Nowadays, the mainstream expression recognition method is still based on 6+1 classification mode to give comparison result. But for the students’ learning statue in classroom situation, to study whether 2019 9th International Conference on Education and Social Science (ICESS 2019) Copyright © (2019) Francis Academic Press, UK DOI: 10.25236/icess.2019. 179 940 these six emotions can help study is still necessary. Base on whether it is conductive to learning, we set up three categories of emotional states, including positive effects on learning, negative effects, and insignificant effects. Camera Installation To dynamically identify each student’s emotional state, it is required to detect each students’ head portrait information in real time. Based on the selected cameras’ visual angle, resolution, size of different classroom, student number, the optimal camera installation location can be plan out. If the classroom is too large, an array of cameras can be set. Fig.1 shows the installation layout of two cameras. figure 1. Camera Installation Layout Facial Expression Recognition Normally, facial expression recognition is consisted of three basic steps. First: face preprocessing; Second: facial emotion feature extraction; Third: emotion classify by classifier. Three steps are shown in Fig 2. Face detection is a computer technology which provides the location and size information of faces in images. According to the information provided by face rectangle figure 2. Facial Expression Recognition In face detection, facial feature points can be precisely located. After that face normalization, expression analysis can be applied. Face normalization aligns the face to a predefined template according to the feature points’ position. The emotion feature extraction process extracts robust face features from normalized face images. Finally, comparing the extracted features with known emotion features, the emotion can be classified.