A Privacy-Preserving Federated-MobileNet for Facial Expression Detection from Images

Abstract

Facial expression recognition is an intriguing research area that has been explored and utilized in a wide range of applications such as health, security, and human-computer interactions. The ability to recognize facial expressions accurately is crucial for human-computer interactions. However, most of the facial expression analysis techniques have so far paid little or no concern to users’ data privacy. To overcome this concern, in this paper, we incorporated Federated Learning (FL) as a privacy-preserving machine learning approach in the field of facial expression recognition to develop a shared model without exposing personal information. The individual models are trained on the different client devices where the data is stored. In this work, a lightweight Convolutional Neural Network (CNN) model called the MobileNet architecture is utilised to detect expressions from facial images. To evaluate the model, two publicly available datasets are used and several experiments are conducted. The result shows that the proposed privacy-preserving Federated-MobileNet approach could recognize facial expressions with considerable accuracy compared to the general approaches.