The use of the internet of things (IoT) is steadily increasing in a wide range of applications. Integration of IoT, computer vision, and artificial intelligence can improve people's daily life in various domains such as smart homes, smart cities, and smart industries. There are a large number of face recognition and face attribute recognition scenarios in reality, and the industry commonly decomposes these tasks, with three models responsible for handling face detection, face recognition, and face attribute recognition. The multi‐model approach requires a lot of computational resources for context switching, while training one model with one dataset is not only complex, but also leads to overfitting of the multi‐model approach. The authors propose a single‐model multi‐task approach, which can complete all tasks using only one model, and thus obtains a great improvement in inference speed, especially in scenes with high face density. After an experimental comparison, our approach saves a maximum of 96% of inference time, 49.5% of memory usage, and 59.7% of CPU time, avoids frequent context switching, and simplifies the training steps while improving the generalization performance of the model.

Benefiting from deep learning, the accuracy of face expression recognition tasks based on convolutional neural networks has been greatly improved. However, the traditional SoftMax activation function lacks the ability to discriminate between classes. To solve this problem, the industry has proposed several activation functions based on softmax, such as A-softmax, LMCL, etc. We investigate the geometric significance of the weights from a fully connected layer and consider the weights as the class centers. By extracting the feature vector of several samples and extending the corresponding means to the weights, the model can develop the ability to recognize custom classes without training, while maintaining the accuracy of the original classification. On the expression task, the original seven-category classification is validated to obtain 97.10% accuracy on the CK+ dataset and 88% accuracy on the custom dataset.

With the application of 5G technology in the field of education, the construction of smart campus has set off a wave of digital transformation. At the same time, the traditional smart campus is also facing the exponential growth of the number of Internet of Things devices, servers, and application terminals, which makes it difficult to achieve flat management. In view of the current difficulties in the construction of smart campus, this paper proposes smart campus architecture based on blockchain technology. Unlike the traditional smart campus architecture, this paper combines the characteristics of decentralization, high confidentiality, and data sharing of block chain with the Internet of Things technology, which greatly reduces the demand for data storage and physical network equipment. The new smart campus architecture plans the application of smart education based on blockchain, and provides a new solution model and research ideas.