A Smart Glasses-Based Real-Time Micro-expressions Recognition System via Deep Neural Network

The rapid development of new communication technologies such as social software and email has brought convenience, but it has also increased the distance between people. This research aims to capture micro-expressions in real-time through smart glasses and uses deep learning technology to develop a real-time emotion recognition system based on RGB color values to improve interpersonal communication. We trained a multi-layer fully deep neural network (DNN) model using the CASME2 dataset to effectively learn the association between facial expressions and emotions. The experimental results of model show that the emotion classification accuracy of the system reaches 95% on several test samples. The system is adapted to run on smart glasses, the emotion recognition results are immediately fed back to the screen of the smart glasses and displayed to the user in the form of an emotion label. The experimental results of system show that the system can achieve accurate emotion recognition, help users better understand the psychological state of the communication object, and improve the communication environment and quality. In addition, the system can also store communication records and emotion analysis results to help users understand their own communication performance and changes in others’ emotions, thus improving communication skills and abilities.