Study on the detection of facial emotion using AlexNet plus LSTM model

For researchers in medical or social psychology, facial emotion knowledge is crucial as it serves as valuable interpretive data. Facial emotion information not only helps in understanding a person's inner thoughts but also enables observation of their likes and dislikes, which is essential for further exploration in medical and social psychology research. This is particularly important when patients are unable to express their thoughts through words or gestures, making facial emotions a valuable tool for doctors to assess and inquire about the condition of patients. In this study, we utilized the facial emotion dataset provided by AffectNet, which comprises over 450,000 photos categorized into 11 different emotions, as the sample data for neural network learning. We proposed a model called AlexNet_Plus_LSTM, which combines convolutional and recurrent layers. Specifically, we incorporated long and short-term memory networks (LSTM), commonly used in time-series situations, to replace the fully connected layer in our proposed model. The model utilizes convolutional neural network (CNN) to learn facial emotion features from static images, and these features are then fed into LSTM to learn the relationships between different facial emotion characteristics, thus improving the accuracy of facial emotion recognition. The results obtained from our research demonstrate that the AlexNet_Plus_LSTM model achieves an accuracy of 76.83%. This research contributes to the field of facial emotion recognition by proposing a novel model that combines convolutional and recurrent layers, and incorporating LSTM to improve accuracy. The findings have potential applications in medical and social psychology research, where facial emotion knowledge can provide valuable insights into understanding human emotions and behavior in various contexts.