# A Hyper-Attentive Multimodal Transformer for Real-Time and Robust Facial Expression Recognition
## 6 Results  
The evaluation of FERONet was conducted across five benchmark datasets, encompassing static, 3D, and video modalities. The model demonstrated consistently high performance in terms of accuracy, temporal consistency, and real-time applicability, confirming its effectiveness in diverse and unconstrained environments Table 4.  
These results establish FERONet as a state-of-the-art model for FER. Notably, on the RAF-DB dataset--characterized by spontaneous, real-world facial images--the model achieved a near-ceiling accuracy of 97.3%, outperforming both FERONET and other  
Figure 5: Training and validation loss curves of FERONet over 150 epochs. The model demonstrates stable convergence with minimal overfitting, highlighting the effectiveness of the training setup and regularization strategies.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Type** & **Classes** & **Train Samples** &
\begin{tabular}{c} **Validation** \\ **Samples** \\ \end{tabular} & **Test Samples** & **Split Strategy** \\ \hline FER-2013 & Static & 7 & 28,709 & 3589 & 3589 & Stratified (80/10/10) \\ \hline RAF-DB & Static & 7 & 23,737 & 2967 & 2968 & Standard protocol [12] \\ \hline CK+ & Video & 7 & 393 & 100 & 100 & Subject-independent split \\ \hline BU-3 DFE & 3D Static & 6 & 1800 & 350 & 350 & Stratified (72/14/14) \\ \hline AFEW & Video & 7 & 997 & 174 & 174 & Standard split [15] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data Distribution Summary.  
transformer-based models. Similarly, the model showed exceptional generalization on CK+ and BU-3 DFE, confirming its capacity to handle both dynamic and structurally rich expressions. Temporal datasets such as AFEW further demonstrated the utility of the temporal decoder with cross-attention, which effectively isolated emotionally salient frames. In addition, inference times were consistently below 16 milliseconds across all datasets, meeting the threshold for real-time deployment on edge devices. Class-wise confusion matrices revealed that the model particularly excelled in differentiating between complex expressions such as fear vs. surprise and anger vs. disgust, which are traditionally challenging due to overlapping facial features. F1-scores remained above 92% for all datasets, indicating balanced precision and recall even in the presence of class imbalance Figure 6.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Dataset** & **Accuracy (\%)** & **F1-Score** & **AUC-ROC** &
\begin{tabular}{c} **Inference** \\ **Time (ms)** \\ \end{tabular} \\ \hline FER-2013 & 94.7 & 94.3 & 0.987 & 14.2 \\ \hline RAF-DB & 97.3 & 97.1 & 0.995 & 13.8 \\ \hline CK+ & 97.1 & 96.9 & 0.993 & 13.5 \\ \hline BU-3 DFE & 95.2 & 95.0 & 0.980 & 14.4 \\ \hline AFEW & 93.1 & 92.5 & 0.973 & 15.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: FERONet achieved the following metrics across datasets.  
Figure 6: Confusion matrix for the FER-2013 dataset showing class-wise prediction accuracy and common misclassifications. FERONet demonstrates high discriminative performance across most emotion classes, with minor confusion observed between fear and happy expressions.