融合多个数据集并用加权采样，提升面部表情识别准确率至82%。
Improving Facial Emotion Recognition through Dataset Merging and Balanced Training Strategies
- 合并CK+、FER+、KDEF三个数据集扩大训练规模
- 通过增广和加权采样缓解少数类别样本不足问题
- 适合关注表情识别数据不平衡问题的研究者
本文提出一种基于深度卷积网络的自动面部表情识别深度学习框架。为提升方法的泛化能力和鲁棒性，通过合并三个公开的面部表情数据集——CK+、FER+ 和 KDEF 来增加数据规模。尽管数据量增加，少数类别仍存在训练样本不足的问题，导致数据不平衡。为此，采用在线与离线增广技术及随机加权采样策略最小化数据不平衡。实验结果表明，该方法在七种基本情绪识别上达到82%的准确率，验证了所提方法在应对数据不平衡问题及提升分类性能方面的有效性。
原文摘要 · Abstract (English)
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.
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