# A Hyper-Attentive Multimodal Transformer for Real-Time and Robust Facial Expression Recognition
## 6 Results
#### Comparison with SOTA Models  
The comparative analysis of facial expression recognition models highlights the effectiveness of the FERONET in SOTA approaches. The evaluation was conducted using three benchmark datasets--FER-2013, CK+, and RAF-DB--assessing four key performance metrics: accuracy, F1-score, AUC-ROC. The results demonstrate that FERONET consistently outperforms competing models across all datasets. On FER-2013, FERONET achieved an accuracy of 92%, surpassing DFEM (89%) and CF-DAN (91%). Similarly, it recorded 95% accuracy on CK+ and RAF-DB, maintaining robustness across different facial expression datasets (Figure 7). The model also exhibited the highest AUC-ROC scores, ranging from 0.98 to 0.99, indicating its strong ability to distinguish between emotional expressions. Furthermore, its inference time, ranging from 15 to 18 ms, was the lowest among all models, making it highly efficient for real-time applications. In comparison to other models, traditional CNN-based approaches such as DFEM [7] and CSE-GResNet [22] demonstrated competitive accuracy but were less efficient in computational speed. Attention-based models like CF-DAN [16] and EmoFormer [26] improved precision and recall yet required higher inference times, making them less suitable for real-time use. Transformer-based architectures, including MRAN [28] and MGET [36], showed promising accuracy but struggled with computational efficiency, limiting their practical application in time-sensitive environments. FERONET's low inference time, especially when compared to models such as HALNet [30] (40 ms) and CF-DAN [16] (45 ms), underscores its suitability for real-world applications where fast and accurate emotion recognition is essential (Figure 8). This efficiency is particularly advantageous in human-computer interactions, security surveillance, and healthcare settings, where timely emotion detection can significantly enhance system responsiveness. The results confirm that FERONET successfully balances high recognition accuracy with computational efficiency. The integration of multi-scale feature fusion and adaptive attention mechanisms plays a key role in improving FER performance, making FERONET a highly effective solution for affective computing applications Table 5.  
From the table, FERONET demonstrates high accuracy and F1-scores across all datasets compared to the traditional DCNNs and performs comparably or better than the newer attention-based and transformer models. Particularly noteworthy is FERONET's AUC-ROC score, which indicates its strong capability in distinguishing between different emotions effectively. The inference time of FERONET is also competitive, showing that the model not only improves accuracy but also maintains operational efficiency, making it suitable for real-time applications where both accuracy and speed are critical. The detailed comparison with SOTA models confirms the effectiveness of FERONET in handling the complexities of FER. By leveraging advanced attention mechanisms and transformer-based enhancements, FERONET offers a significant improvement over traditional models and holds its ground against newer technologies. These results suggest that FERONET is well-suited for deployment in diverse and dynamic environments, providing reliable and swift facial expression recognition. Future enhancements will focus on expanding FERONET's capabilities in handling more granular emotions and operating under even more challenging conditions. Figure 7: Representative examples of facial expressions across datasets.