| Summary: | Facial expression is considered one of the most important ways of communication and human response to its environment. Recognition of facial emotional expression is used in many research fields, such as psychological studies, robotics, identity recognition, disease diagnosis, etc. This paper, due to the importance of recognition of facial emotional expression, presents a new and efficient method based on learning and recognition of facial emotional expression, which is a combination of the limbic system of the human brain and the convolutional neural network. In the proposed model, first, the facial emotional expression images are normalized, and after reducing the dimensions of implicit features, proper and practical features are classified using the convolutional brain emotional learning (CBEL) model, and facial emotional expressions are recognized. Moreover, the performance of the proposed model is compared with BEL, CNN, SVM, MLP, and KNN models. After examining the results, it is concluded that the accuracy of facial emotional expression recognition rate is higher in the CBEL learning model.
|
|---|