Deep network expression recognition with transfer learning in UAV-enabled B5G/6G networks.

To better apply deep convolutional neural networks for expression recognition in UAV-enabled B5G/6G networks, we propose a deep network expression recognition method based on transfer learning and fine-tuning on facial expression datasets. Initially, we train our model on a large-scale facial attribute dataset and subsequently fine-tune it on a facial expression dataset. This strategy effectively lowers the high costs and dependence associated with annotating facial expression datasets, while enhancing the accuracy and training speed of our model. This method is particularly suited for UAVs equipped with on-board cameras and image processing capabilities, enabling real-time expression recognition for various applications such as crowd monitoring, search and rescue, and human-UAV interaction. Compared to traditional methods that train exclusively on facial expression datasets, our method significantly reduces the number of training iterations on facial expression datasets and significantly improves the generalization ability of the model, especially for UAV applications. We use a large-scale facial attribute dataset, which is more closely related to the facial expression recognition task, as our source dataset, forming a contrast with methods that typically use a facial recognition dataset as the transfer learning source dataset. The experimental results on the CK + dataset, integrated with UAV-enabled B5G/6G networks, show that our method achieves a facial expression recognition accuracy of 97.6%, a significant improvement over the 97.3% accuracy rate of methods that only train on facial expression datasets, with less training time as well. [ABSTRACT FROM AUTHOR]