Automatic facial expression recognition (FER) is a fundamental topic in computer vision. Many studies have indicated that facial emotion changes are strongly related to certain regions of interest (ROIs), such as the mouth, eyes, eyebrows, and nose; therefore, the features of these facial ROIs are very important for identifying expressions. Since Gabor filters are very efficient in extracting visual content, Gabor orientation filters (GoFs) modulated by Gabor kernels and traditional convolutional filters can capture such ROI information better than conventional convolutional filters. Consequently, this letter presents a light Gabor convolutional network (GCN) consisting of only four Gabor convolutional layers and two linear layers for FER tasks. Extensive experiments on the FER2013, FERPlus and Real-world Affective Faces (RAF) databases demonstrate that the proposed method achieves good recognition accuracy and requires very low computational costs. The source code can be found at https://github.com/general515/Facial_Expression_Recognition_Using _GCN.

中文翻译：

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使用 Gabor 卷积网络进行快速有效的面部表情识别

自动面部表情识别 (FER) 是计算机视觉中的一个基本主题。许多研究表明，面部情绪变化与某些感兴趣区域 (ROI) 密切相关，例如嘴巴、眼睛、眉毛和鼻子；因此，这些面部 ROI 的特征对于识别表情非常重要。由于 Gabor 滤波器在提取视觉内容方面非常有效，因此由 Gabor 内核和传统卷积滤波器调制的 Gabor 方向滤波器 (GoF) 可以比传统卷积滤波器更好地捕获此类 ROI 信息。因此，这封信提出了一个轻型 Gabor 卷积网络 (GCN)，它仅由四个 Gabor 卷积层和两个用于 FER 任务的线性层组成。FER2013 上的大量实验，FERPlus 和真实世界情感人脸 (RAF) 数据库表明，所提出的方法实现了良好的识别精度，并且需要非常低的计算成本。源代码可以在 https://github.com/general515/Facial_Expression_Recognition_Using _GCN 找到。