## 1 Introduction
POSTER++ reached 92.21% on RAF-DB, 67.49% on AffectNet (7 cls) and 63.77% on AffectNet (8 cls), respectively, using only 8.4 G floating point operations (FLOPs) and 43.7 M parameters (Param). [PERSON] et al. [38] propose CF-DAN which comprises three parts:(1) a cross-fusion grouped dual-attention mechanism to refine local features and obtain global information; (2) a proposed C\({}^{2}\) activation function construction method, which is a piecewise cubic polynomial with three degrees of freedom,requiring less computation with improved flexibility and recognition abilities, which can better address slow running speeds and neuron inactivation problems; and (3) a closed-loop operation between the self-attention distillation process and residual connections to suppress redundant information and improve the generalization ability of the model. The recognition accuracies on the RAF-DB, FERPlus, and AffectNet datasets were 92.78%, 92.02%, and 63.58%, respectively. Despite these advancements, a significant knowledge gap remains: most existing lightweight FER models are designed by adapting general-purpose lightweight backbones, without specifically optimizing for the unique structural and semantic characteristics of facial expression images. As a result, these models often fail to fully utilize local expression cues--especially from key facial regions such as the eyes, mouth, and eyebrows--which are critical for emotion discrimination. In addition, many approaches either focus solely on architectural compression or rely heavily on post-processing modules, with limited integration of spatial-semantic guidance into the feature extraction process itself. To address these limitations, we propose LightExNet, a novel lightweight CNN tailored specifically for facial expression recognition. The design of LightExNet builds upon the MobileNetV2 backbone, as MobileNet V2 offers both high accuracy and a small number of parameters, making it well suited for lightweight facial expression recognition. But introduces three key innovations to enhance expression-specific feature extraction and classification under constrained computational budgets:  
* Shallow-Deep Feature Fusion: By integrating low-level shallow features with high-level deep representations, the network retains fine-grained local details while gaining semantic abstraction, thus improving multiscale perception and alleviating gradient vanishing in deeper layers. * Improved Channel and Spatial Attention Mechanism: A task-specific attention module is introduced to assign higher weights to emotionally salient regions, enhancing the model's ability to focus on expression-relevant facial components. * Joint Loss Function with Center Loss Optimization: An improved center loss is employed to enhance intra-class compactness and inter-class separability, thereby improving classification performance without increasing model complexity. The proposed LightExNet is evaluated on three benchmark datasets: FER2013, CK+, and RAF-DB. Experimental results show that LightExNet achieves competitive accuracy (69.17%, 97.37%, and 85.97%, respectively) with only 3.27 M parameters and 298.27 M FLOPs, outperforming several existing mainstream lightweight FER models in both accuracy and computational efficiency. In summary, this work bridges the gap between lightweight model design and expression-specific feature optimization, contributing a practical solution for real-time FER deployment on resource-limited devices.