Research on a Lightweight Real-Time Facial Expression Recognition System Based on an Improved Mini-Xception Algorithm

This paper proposes a lightweight facial expression recognition model based on an improved Mini-Xception algorithm to address the issue of deploying existing models on resource-constrained devices. The model achieves lightweight facial expression recognition, particularly for elder-oriented applications, by introducing depthwise separable convolutions, residual connections, and a four-class expression reconstruction. These designs significantly reduce the number of parameters and computational complexity while maintaining high accuracy. The model achieves an accuracy of 79.96% on the FER2013 dataset, outperforming various other popular models, and enables efficient real-time inference in standard CPU environments.