融合注意力与残差结构，提升人脸表情识别准确率与模型收敛性。
ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition
- 采用卷积+SE注意力+残差块的混合架构，强化关键特征表达。
- 在4个数据集上最高达94.76%准确率，均超越现有模型。
- 适合关注情绪识别性能优化的研究者与工业应用开发者。
人类面部是无声的沟通者，通过表情传递情感与思想。近年来计算机视觉的发展使人脸表情识别技术取得显著进展，让机器能够解析面部线索的细微差别。本文提出ResEmoteNet，一种结合卷积网络、Squeeze-Excitation（SE）模块与残差网络的新型深度学习架构。SE模块可选择性聚焦人脸关键特征，增强表征能力并抑制无关信息，从而降低损失并提升整体性能。同时，将SE模块与三个残差块结合，使模型在深层网络中学习更复杂的特征表示。我们在四个开源数据集FER2013、RAF-DB、AffectNet-7和ExpW上评估了ResEmoteNet，分别达到79.79%、94.76%、72.39%和75.67%的准确率，在所有数据集上均优于当前最先进模型。代码已公开于https://github.com/ArnabKumarRoy02/ResEmoteNet。
原文摘要 · Abstract (English)
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.
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