The Application of Real-time Emotion Recognition in Video Conferencing

Video conferencing has become a crucial tool for global remote collaboration, but existing platforms have limitations in capturing and conveying participants' emotions. Real-time emotion recognition technology, by combining computer vision, deep learning, and temporal analysis, can automatically analyze and identify emotional changes in participants, addressing this gap. This paper first introduces the core technical processes of emotion recognition, including face detection, feature extraction, and emotion classification, with a focus on the technical details of using Convolutional Neural Networks (CNNs) for feature extraction and classification algorithms. To enhance the system's temporal dynamics, the paper also presents methods for capturing emotional changes based on Long Short-Term Memory networks (LSTMs) or Temporal Convolutional Networks (TCNs). In particular, we use a Softmax classifier for probability estimation of emotions, coupled with temporal analysis methods for real-time engagement assessment in meetings. Furthermore, this paper discusses the system implementation under an edge computing and cloud collaboration framework to optimize real-time performance and computational efficiency while proposing security strategies focused on privacy protection, such as homomorphic encryption and federated learning. Through specific application examples, this paper demonstrates the significant role of real-time emotion recognition technology in video conferencing and its potential impact on future remote communication modes.