Research on Emotion Recognition Based on Deep Learning Mixed Modalities

With the rapid development of artificial intelligence and machine learning in recent years, emotion recognition has gradually become an important research topic. Emotion recognition in one direction has a good research foundation after long-term development, and from multiple directions, more effective information can be extracted, thereby improving the accuracy of emotion recognition. This paper analyzes from the perspective of emotional recognition of physiological signals such as brainwave signals and facial emotion recognition, respectively, preprocessing, feature extraction, SVM feature classification, LSTM combined with convolutional neural network emotion recognition for the acquired signals. And the accuracy of mixed-modal emotion recognition is compared. Compared with single facial expression emotion recognition, mixed-modal emotion recognition extracts more feature information and has a higher accuracy.