| Abstract |
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. © 2022 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. |
