Papers › ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition
ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition
Arnab Kumar Roy, Hemant Kumar Kathania, Adhitiya Sharma, Abhishek Dey, Md. Sarfaraj Alam Ansari
The human face is a silent communicator, expressing emotions and thoughts through its facial expressions. With the advancements in computer vision in recent years, facial emotion recognition technology has made significant strides, enabling machines to decode the intricacies of facial cues. In this work, we propose ResEmoteNet, a novel deep learning architecture for facial emotion recognition designed with the combination of Convolutional, Squeeze-Excitation (SE) and Residual Networks. The inclusion of SE block selectively focuses on the important features of the human face, enhances the feature representation and suppresses the less relevant ones. This helps in reducing the loss and enhancing the overall model performance. We also integrate the SE block with three residual blocks that help in learning more complex representation of the data through deeper layers. We evaluated ResEmoteNet on four open-source databases: FER2013, RAF-DB, AffectNet-7 and ExpW, achieving accuracies of 79.79%, 94.76%, 72.39% and 75.67% respectively. The proposed network outperforms state-of-the-art models across all four databases. The source code for ResEmoteNet is available at https://github.com/ArnabKumarRoy02/ResEmoteNet.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Facial Expression Recognition (FER) | AffectNet | ResEmoteNet | Accuracy (7 emotion) | 72.93 | #38 of 50 | Archive leaderboard | report |
| Facial Expression Recognition (FER) | ExpW | ResEmoteNet | Accuracy | 75.67 | #1 of 1 | Archive leaderboard | report |
| Facial Expression Recognition (FER) | FER2013 | ResEmoteNet | Accuracy | 79.79 | #3 of 17 | Archive leaderboard | report |
| Facial Expression Recognition (FER) | RAF-DB | ResEmoteNet | Overall Accuracy | 94.76 | #1 of 35 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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