Exploiting Transfer Learning for Emotion Recognition Under Cloud-Edge-Client Collaborations

Emerging virtual reality/augmented reality games and self-driving cars necessitate accurate/responsive/private emotion recognition. Usually, traditional emotion recognition models are deployed at central servers, which results in the lack of abilities in generalization and covering the individual variation of clients. This paper proposes a responsive, localized, and private transfer learning based emotion recognition framework under the cloud-edge-client collaborations. Additionally, a 3-dimensional channel mapping method is designed to aggregate features extracted from electroencephalogram (EEG) signals for the generic emotion recognition model, which is further localized and personalized using transfer learning. Simulation results validate the performance of the proposed TLER framework in reducing model training time and improving emotion recognition accuracy.

Automated summary

This paper proposes a novel emotion recognition framework that is responsive, localized, and private, utilizing cloud-edge-client collaborations. A 3D channel mapping method is designed to aggregate features extracted from EEG signals to enhance the generic emotion recognition model. Simulation results demonstrate the effectiveness of the proposed TLER framework in reducing model training time and improving emotion recognition accuracy.