Recognizing learning emotion based on convolutional neural networks and transfer learning

Abstract Learning effectiveness is normally analyzed by data collection through tests or questionnaires. However, instant feedback is usually not available. Learners’ facial emotion and learning motivation has a positive relationship. Therefore, the system identifying learners’ facial emotions can provide feedback that teachers can understand students’ learning situation and provide help or improve teaching strategy. Studies have found that convolutional neural networks provide a good performance in basic facial emotion recognition. Convolutional neural networks do not require manual design features like traditional machine learning, they automatically learn the necessary features of the entire image. This article improves the FaceLiveNet network with low and high accuracy in basic emotion recognition, and proposes the framework of Dense_FaceLiveNet. We use Dense_FaceLiveNet for two-phases of transfer learning. First, from the relatively simple data JAFFE and KDEF basic emotion recognition model transferring to the FER2013 basic emotion dataset and obtained an accuracy of 70.02%. Secondly, using the FER2013 basic emotion recognition model transferring to learning emotion recognition model, the test accuracy rate is as high as 91.93%, which is 12.9% higher than the accuracy rate of 79.03% without using the transfer learning model, which proves that the use of transfer learning can effectively improve the recognition accuracy of learning emotion recognition model. In addition, in order to test the generalization ability of the Learning Emotion Recognition Model, videos recorded by students from a national university in Taiwan during class learning were used as test data. The original database of learning emotions did not consider that students would have exceptions such as over eyebrows, eyes closed and hand hold the chin etc. To improve this situation, after adding the learning emotion database to the images of the exceptions mentioned above, the model was rebuilt, and the recognition accuracy rate of the model was 92.42%. By comparing the output of maps, the rebuilt model does have the characteristics of success in learning images such as eyebrows, chins, and eyes closed. Furthermore, after combining all the students’ image data with the original learning emotion database, the model was rebuilt and obtained the accuracy rate reached 84.59%. The result proves that the Learning Emotion Recognition Model can achieve high recognition accuracy by processing the unlearned image through transfer learning. The main contribution is to design two-phase transfer learning for establishing the learning emotion recognition model and overcome the problem for small amounts of learning emotion data. Our experiment results have shown the performance improvement of two-phase transfer learning.