Abstract
This paper briefly introduced the support vector machine (SVM) based and convolutional neural network (CNN) based healthy emotion recognition method, then improved the traditional CNN by introducing Long Short Term Memory (LSTM), and finally carried out simulation experiments on three emotion recognition models, the SVM, traditional CNN, and improved CNN models, in the self-built face database. The results showed that the CNN model converged faster in training and had a smaller error when it was stable after introducing LSTM; compared with the SVM and traditional CNN models, the improved CNN had a higher recognition accuracy for facial expressions; the time consumed by the improved CNN model was the shortest in both training and testing stages.
Highlights
With the progress of science and technology and the improvement of computer performance, artificial intelligence appeared and has been widely used in mechanical operation fields, such as translation, image recognition, and classification, which are not difficult but highly repetitive [1]
Atkinson et al [5] proposed a feature-based emotion recognition model based on an electroencephalogram, which combined the mutual information-based feature selection method with kernel classifier to improve the accuracy of emotion classification tasks
This paper briefly introduced the emotion recognition method based on support vector machine (SVM) and convolutional neural network (CNN), improved the traditional CNN by introducing Long Short Term Memory (LSTM), and carried out simulation experiments on three emotion recognition models, the SVM, traditional CNN, and improved CNN models, in the ORT human face database and self-built face database
Summary
With the progress of science and technology and the improvement of computer performance, artificial intelligence appeared and has been widely used in mechanical operation fields, such as translation, image recognition, and classification, which are not difficult but highly repetitive [1]. Human beings express their emotions in various forms, including actions, language, physiological signals, and facial expressions These emotions usually reflect their psychological state, especially physiological signals and facial expressions. Facial expression can directly reflect people’s emotions, and the changes of emotions reflect the state of mental health. When mental health is judged by the emotion reflected by the facial expression, the manual observation needs rich clinical experience and has low efficiency. Artificial intelligence has a fast computing speed, and it can extract relevant feature rules from face images more effectively and judge whether people’s emotions are in a healthy state. The results showed that the method could deal with speed changes and continuous head pose changes to realize fast emotion recognition. This paper briefly introduced the emotion recognition method based on SVM and convolutional neural network (CNN), improved the traditional CNN by introducing Long Short Term Memory (LSTM), and carried out simulation experiments on three emotion recognition models, the SVM, traditional CNN, and improved CNN models, in the ORT human face database and self-built face database
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