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We evaluate the effectiveness of our proposed method on the Real-world Affective Faces Database (RAF-DB), a widely adopted benchmark for facial expression recognition. The dataset has been carefully curated to cover diverse conditions, including variations in age, gender, ethnicity, head pose, illumination, and occlusion, making it highly representative of real-world FER challenges. It is also important to note that the dataset is highly imbalanced, with an imbalance ratio of 16.78, reflecting substantial variation in class frequencies.
In our experimental evaluation, we employed ResNet as the backbone network, following established practices in recent facial expression recognition literature to ensure fair comparison with state-of-the-art methods that utilize similar pre-training strategies. At a first glance, Figure 7 depicts the training and validation performance of the proposed model across 100 epochs. The validation curves closely follow the trends observed in the training curves. Leveraging pre-trained weights, the model converges rapidly, with training loss nearing zero by epoch 30. Validation curves stabilize after ~20 epochs, but accuracy remains competitive (around 88%), demonstrating effective generalization to unseen data.
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IEEE/ICACT20260151 Slide.13
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