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.

中文翻译：

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在云-边缘-客户端协作下利用迁移学习进行情绪识别


新兴的虚拟现实/增强现实游戏和自动驾驶汽车需要准确/灵敏/私密的情感识别。传统的情感识别模型通常部署在中央服务器上，缺乏泛化能力和覆盖客户端个体差异的能力。本文提出了一种云-边-端协作下的响应式、本地化、私有的基于迁移学习的情感识别框架。此外，还设计了一种 3 维通道映射方法来聚合从脑电图 (EEG) 信号中提取的特征，用于通用情感识别模型，并使用迁移学习进一步本地化和个性化。仿真结果验证了所提出的 TLER 框架在减少模型训练时间和提高情绪识别准确性方面的性能。