Lightweight Dynamic Perception Facial Expression Recognition for Power Business Hall Scenarios

Aiming at the problems of low facial expression recognition accuracy, high computational complexity, and difficulty in edge deployment caused by variable illumination, diverse head poses, severe partial occlusions, and the long-tail distribution of expression categories in complex power business hall scenarios, this paper presents an engineering-oriented lightweight dynamic perception facial expression recognition method for complex power business hall scenarios. The proposed method adopts an end-to-end joint face detection and expression classification framework built upon the anchor-free CenterNet architecture. In terms of technical implementation, this paper primarily focuses on the integration and adaptation of existing techniques to meet scenario-specific requirements; integrates a specially designed Multi-Scale Fusion Deformable Large Kernel Attention (MSF-DLKA) module to enhance multi-scale perception of subtle expression deformations under varying poses; designs a Task-Aware Dynamic Detection Head (TADDH) with decoupled spatial-channel attention to separately adapt the feature requirements of localization and classification subtasks; directly employs the off-the-shelf Label-Distribution-Aware Margin loss (LDAM Loss) to alleviate class imbalance; and combines three established compression techniques—structured pruning, quantization-aware training, and knowledge distillation—into a progressive lightweight pipeline for edge deployment. Experiments on two public micro-expression datasets, CASME II and SAMM, as well as a self-built electric power business hall scenario dataset (PBHD), show that the proposed method achieves [email protected] of 93.2%, 93.9%, and 91.7%, and macro-F1 of 0.929, 0.936, and 0.914 on the three datasets, respectively, while using only 13.5 M parameters and attaining a single-image inference time of 28.9 ms on the NVIDIA Jetson AGX Orin. The paper provides a feasible solution for real-time expression perception in resource-constrained scenarios such as power business halls.