Research on Emotion Recognition Based on Deep Learning for Mental Health

Xianglan Peng


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.

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