FE-SpikeFormer: A Camera-Based Facial Expression Recognition Method for Hospital Health Monitoring

Abstract

Facial expression recognition has emerged as a critical research area in health monitoring, enabling healthcare professionals to assess patients' emotional and psychological states for timely intervention and personalized care. However, existing methods often struggle to balance computational accuracy with energy efficiency. To address this challenge, this paper proposes FE-SpikeFormer — a high-accuracy, low-energy, and deployment-friendly Spiking Neural Network (SNN) for facial emotion recognition. The proposed architecture comprises three key components: the initial convolution module, the spiking extraction block, and the spiking integration block. These three modules collectively support detailed and contextual feature extraction, promote spatial feature integration, and strengthen the representational capacity of spiking signals. Meanwhile, a joint verification is conducted in both controlled laboratory settings and real-world hospital scenarios. Experimental results demonstrate that FE-SpikeFormer achieves top-three recognition accuracy among state-of-the-art methods, while utilizing only 6.93 million parameters. Moreover, it exhibits strong robustness against various noise conditions, underscoring its potential for practical deployment in healthcare environments.