Real-time facial expression recognition on Res-MobileNetV3

Artificial intelligence, such as deep learning technology, has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situations. To pursue a high facial expression recognition accuracy, the network model of deep learning is generally designed to be very deep while the model's real-time performance is typically constrained and limited. With MobileNetV3, a lightweight model with a good accuracy, a further study is conducted by adding a basic ResNet module to each of its existing modules and an SSH (Single Stage Headless Face Detector) context module to expand the model's perceptual field. In this article, the enhanced model named Res-MobileNetV3, could alleviate the subpar of real-time performance and compress the size of large network models, which can process information at a rate of up to 33 frames per second. Although the improved model has been verified to be slightly inferior to the current state-of-the-art method in aspect of accuracy rate on the publically available face expression datasets, it can bring a good balance on accuracy, real-time performance, model size and model complexity in practical applications.