Recognition of Teachers’ Facial Expression Intensity Based on Convolutional Neural Network and Attention Mechanism

The evaluations of traditional teaching quality are mainly subjective, and there is a lack of fine-grained objective data to support the evaluation of teaching states in the classroom. In this paper, an intensity-based facial expression dataset is proposed and named EIDB-13, which contains 13 kinds and 10393 facial images collected from thousands of individuals and existing facial expression datasets. Convolutional neural network (CNN) and attention mechanism are combined to recognize facial expressions. Migration learning is used to solve over-fitting problem in the process of training deep network based on the small sample dataset. InceptionResNetV2 is employed as migration network. Furthermore, an InceptionResNetV2+CBAM network proposed extract similar feature information among facial expressions and it outperforms the network without attention mechanisms. Experiments show a classification accuracy rate of 78% on the intensity-based facial expression dataset EIDB-13 and of 88% on the public macro expression dataset RAF-DB. Combining facial expression recognition technology into teaching is a key foundation to study teaching quality on the intensity of teacher's expression.