关键词: 连续维度情感识别, ConvLSTM, 深度学习, 学业情绪, 维度情感数据库

Abstract:

Academic emotions can affect and regulate learners’ attention, memory, thinking and other cognitive activities. Automatic emotion recognition is the basis of emotion interaction and instructional decision in intelligent learning environment. At present, the research of emotion recognition mainly focuses on the recognition of discrete emotions, which is discontinuous in the timeline, and cannot accurately depict the evolution process of students’ academic emotions. In order to solve this problem, this paper establishes the dimensional emotional database of middle school students based on the crowd-sourcing method in the real online learning situation. And a deep learning analysis model based on continuous dimensional affective prediction is designed. In the experiment, learning materials that stimulate students’ academic emotions are identified according to students’ learning styles firstly. And then 32 experimenter are recruited for independent online learning and collecting real-time facial images. Next, dimensional database with 157 students’ academic emotion videos and 2 178 students’ facial expressions is obtained by the two-denationalization for each video emotion. Finally, a ConvLSTM net-based dimensional emotion model is established and tested on the dimensional emotion database for middle school students. The mean value of the Concordance Correlation Coefficient（CCC） is 0.581. Meanwhile, the mean value of the uniform correlation coefficient is 0.222 after the experiment on Aff-Wild database. The experiment shows that the dimension-based emotion model proposed in this paper improves the CCC correlation coefficient index by 7.6% to 43.0% in the dimension-based emotion recognition of Aff-Wild database.

Key words: continuous dimension emotion recognition, ConvLSTM, deep learning, academic emotion, dimensional emotion database