Lightweight facial expression estimation for mobile computing in portable device

Facial expression recognition has been studied for many years, especially with the development of deep learning. However, the existing researches still have the following two issues. Firstly, the intensity of facial expression is neglected. Secondly, the deep learning based approaches cannot be directly deployed in the devices with limited resources. In order to tackle these two issues, this paper proposes a lightweight facial expression estimation method using a shallow ordinal regression algorithm, which is deployed in a portable smart device for mobile computing in IoTs. Compared with classification based facial expression recognition methods, ordinal regression considers the intensity of facial expression to achieve better mean absolute error (MAE), which is validated by experiments on several public facial expression datasets. The simulation in portable device also demonstrates its effectiveness for mobile computing.