# Update class center using momentum-based smoothing
## 4 Discussion
### Limitations and Future Directions  
Although LightExNet achieves competitive performance on Fer2013, CK+, and RAF-DB, there are several directions to further enhance its robustness and practicality:  
* Limitations on Imbalanced Datasets  
Despite the overall effectiveness of the proposed model, certain limitations remain, particularly when applied to datasets with highly imbalanced class distributions such as FER2013. As illustrated in the confusion matrix, the recognition performance varies significantly across emotion categories. Emotions with more training samples, such as Happy, Surprise, and Neutral, achieve relatively high accuracy (e.g., 0.89, 0.87, and 0.74, respectively). In contrast, underrepresented classes such as Fear and Sad exhibit lower accuracy (0.51 and 0.57), and Disgust is frequently misclassified as Angry (19%) or Fear (5%).  
These results suggest that the model tends to be biased toward majority classes, which is a common issue in deep learning when trained on imbalanced data. To address this limitation, future work could explore strategies such as oversampling of minority classes, use of class-balanced loss functions (e.g., focal loss), or synthetic data augmentation techniques to improve the representation of underrepresented emotions. Additionally, transfer learning from more balanced or diverse datasets may help enhance the model's generalizability across emotion categories.  
* Extended Dataset EvaluationThe current experiments focus on three mainstream datasets, but testing on larger and more diverse datasets (e.g., AffectNet for in-the-wild expressions, or JAFFE for cross-cultural analysis) would better validate the model's generalization ability. Future work will include these datasets and investigate domain adaptation techniques to address potential distribution shifts.  
* Loss Function Optimization  
While the improved center loss in LightExNet effectively enhances feature discrimination, other advanced loss functions (e.g., ArcFace for angular margin optimization or adaptive weight-based losses for imbalanced data) could further refine intra-class compactness. A comparative study of these losses will be conducted to balance accuracy and computational cost.
* Sensitivity to Occlusion:  
The current model is trained primarily on datasets with relatively clean facial images. In scenarios involving partial occlusion (e.g., masks, hands, glasses), the model's accuracy may degrade due to the loss of critical facial features. Future work may incorporate occlusion-aware training strategies or synthetic occlusion augmentation to improve robustness.  
* Non-Frontal Pose Handling:  
The model assumes near-frontal facial alignment for optimal performance. Although some degree of pose variation is tolerated, performance may decrease on non-frontal or profile views. Integrating pose normalization or multi-view learning could help address this issue.  
* Real-Time Constraints in Ultra-Low-End Devices:  
Although LightExNet performs well on mid-tier devices (e.g., Redmi Note 13 5G), its inference time may not fully meet real-time requirements on extremely resource-constrained platforms such as entry-level microcontrollers or legacy smartphones without GPU/NPU support. Future improvements may consider model quantization or dynamic computation strategies.  
In summary, LightExNet demonstrates strong performance in static image-based FER tasks, offering a compelling trade-off between accuracy and computational efficiency. The results validate the proposed architectural innovations and provide a solid foundation for future enhancements aimed at broader deployment and improved robustness across complex real-world scenarios.