Improving Facial Emotion Recognition through Dataset Merging and Balanced Training Strategies
This work addresses data imbalance in facial emotion recognition, but it is incremental as it applies existing techniques to a merged dataset.
The paper tackled facial emotion recognition by merging three datasets and using balanced training strategies to address data imbalance, achieving 82% accuracy on seven basic emotions.
In this paper, a deep learning framework is proposed for automatic facial emotion based on deep convolutional networks. In order to increase the generalization ability and the robustness of the method, the dataset size is increased by merging three publicly available facial emotion datasets: CK+, FER+ and KDEF. Despite the increase in dataset size, the minority classes still suffer from insufficient number of training samples, leading to data imbalance. The data imbalance problem is minimized by online and offline augmentation techniques and random weighted sampling. Experimental results demonstrate that the proposed method can recognize the seven basic emotions with 82% accuracy. The results demonstrate the effectiveness of the proposed approach in tackling the challenges of data imbalance and improving classification performance in facial emotion recognition.