Real-Time Facial Expression Recognition on Res-MobileNetV3  

作　　者：Li Beibei Zhu Jiansheng Li Suwen Dai Linlin Yan Zhiyuan Ma Liangde 

机构地区：[1]Postgraduate Department,China Academy of Railway Sciences,Beijing 100081,China [2]Institute of Computing Technologies,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China [3]Department of Science,Technology and Information,CHINA RAILWAY,Beijing 100844,China [4]Standards&Metrology Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China [5]Railway Infrastructure Inspection Center,China Railway,Beijing 100081,China

出　　处：《China Communications》2025年第3期54-64,共11页中国通信(英文版)

基　　金：supported by China Academy of Railway Sciences Corporation Limited(No.2021YJ127).

摘　　要：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.

关 键 词：artificial intelligence facial expression recognition MobileNetV3 ResNet SSH 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]