# A Hyper-Attentive Multimodal Transformer for Real-Time and Robust Facial Expression Recognition
## 7 Discussion  
The experimental results presented in the preceding section underscore the efficacy of FERONet as a robust and real-time solution for FER across diverse visual domains. Beyond quantitative improvements in accuracy and latency, the architecture offers several compelling advantages rooted in its design philosophy--namely modularity, multimodality, and interpretability. In this section, we reflect on the broader implications of the model design, its behavior across different evaluation conditions, and its potential applications. One of the most prominent outcomes is the model consistency in performance across both static and temporal datasets, a direct consequence of the carefully integrated temporal decoder and hierarchical attention design. While prior models such as FERONET and EMO \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dataset** & **Accuracy** & **F1-Score** & **AUC-ROC** & **Inference Time** \\ \hline \multirow{3}{*}{EmoFormer [26]} & FER-2013 & 91\% & 92\% & 0.95 & 38 ms \\ \cline{2-6}  & CK+ & 91\% & 92\% & 0.91 & 35 ms \\ \cline{2-6}  & RAF-DB & 91\% & 92\% & 0.94 & 35 ms \\ \hline \multirow{3}{*}{Edge-AI-Driven} & FER-2013 & 90\% & 89\% & 0.95 & 20 ms \\ \cline{2-6}  & CK+ & 91\% & 90\% & 0.94 & 22 ms \\ \cline{2-6}  & RAF-DB & 90\% & 89\% & 0.95 & 20 ms \\ \hline \multirow{3}{*}{MRAN [28]} & FER-2013 & 87\% & 88\% & 0.89 & 21 ms \\ \cline{2-6}  & CK+ & 85\% & 84\% & 0.79 & 22 ms \\ \cline{2-6}  & RAF-DB & 87\% & 87\% & 0.88 & 21 ms \\ \hline \multirow{3}{*}{Poster++ [29]} & FER-2013 & 90\% & 89\% & 0.95 & 29 ms \\ \cline{2-6}  & CK+ & 89\% & 88\% & 0.93 & 31 ms \\ \cline{2-6}  & RAF-DB & 90\% & 90\% & 0.95 & 35 ms \\ \hline \multirow{3}{*}{HALNet [30]} & FER-2013 & 90\% & 89\% & 0.96 & 40 ms \\ \cline{2-6}  & CK+ & 89\% & 87\% & 0.92 & 37 ms \\ \cline{2-6}  & RAF-DB & 88\% & 89\% & 0.93 & 42 ms \\ \hline \multirow{3}{*}{SlowR50-SA [32]} & FER-2013 & 87\% & 89\% & 0.91 & 29 ms \\ \cline{2-6}  & CK+ & 88\% & 88\% & 0.91 & 30 ms \\ \cline{2-6}  & RAF-DB & 86\% & 85\% & 0.89 & 29 ms \\ \hline \multirow{3}{*}{CoT [33]} & FER-2013 & 85\% & 85\% & 0.88 & 34 ms \\ \cline{2-6}  & CK+ & 86\% & 86\% & 0.89 & 32 ms \\ \cline{2-6}  & RAF-DB & 89\% & 87\% & 0.88 & 28 ms \\ \hline \multirow{3}{*}{MMATrans [34]} & FER-2013 & 87\% & 86\% & 0.85 & 30 ms \\ \cline{2-6}  & CK+ & 87\% & 87\% & 0.87 & 22 ms \\ \cline{2-6}  & RAF-DB & 86\% & 85\% & 0.82 & 26 ms \\ \hline \multirow{3}{*}{FER-former [35]} & FER-2013 & 85\% & 85\% & 0.79 & 37 ms \\ \cline{2-6}  & CK+ & 82\% & 82\% & 0.75 & 29 ms \\ \cline{2-6}  & RAF-DB & 85\% & 86\% & 0.80 & 33 ms \\ \hline \multirow{3}{*}{MGET [36]} & FER-2013 & 88\% & 87\% & 0.89 & 25 ms \\ \cline{2-6}  & CK+ & 87\% & 85\% & 0.87 & 39 ms \\ \cline{1-1} \cline{2-6}  & RAF-DB & 87\% & 88\% & 0.88 & 26 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Former performed well on static images, they often failed to generalize under temporal inconsistency, motion blur, or incomplete expressions in video sequences. By introducing a bidirectional temporal encoder fused with cross-attention, FERONet effectively mitigates these issues, demonstrating its ability to localize affective salience over time. Notably, on the AFEW dataset, which is known for its extreme variability, the model not only maintained accuracy above 93% but also exhibited strong temporal coherence, as evidenced by high F1-scores and stable confidence trajectories. Another critical strength lies in the model resilience to occlusion and pose variation, two of the most persistent challenges in in-the-wild FER applications. Through the integration of occlusion-aware feature suppression and pose normalization based on 3D landmark warping, FERONet preserves semantic fidelity even when conventional spatial cues are partially unavailable. The self-reconstruction loss further regularizes learning by encouraging the model to infer missing patterns, resulting in robust generalization without the need for hand-engineered occlusion masks or explicit alignment steps.