Micro-expression recognition is a substantive cross-study of psychology and computer science, and it has a wide range of applications (e.g., psychological and clinical diagnosis, emotional analysis, criminal investigation, etc.). However, the subtle and diverse changes in facial muscles make it difficult for existing methods to extract effective features, which limits the improvement of micro-expression recognition accuracy. Therefore, we propose a multi-scale joint feature network based on optical flow images for micro-expression recognition. First, we generate an optical flow image that reflects subtle facial motion information. The optical flow image is then fed into the multi-scale joint network for feature extraction and classification. The proposed joint feature module (JFM) integrates features from different layers, which is beneficial for the capture of micro-expression features with different amplitudes. To improve the recognition ability of the model, we also adopt a strategy for fusing the feature prediction results of the three JFMs with the backbone network. Our experimental results show that our method is superior to state-of-the-art methods on three benchmark datasets (SMIC, CASME II, and SAMM) and a combined dataset (3DB).

微表情识别是心理学和计算机科学的实质性交叉研究，具有广泛的应用（例如，心理和临床诊断，情感分析，犯罪调查等）。然而，面部肌肉的细微变化使得现有方法难以提取有效特征，这限制了微表情识别精度的提高。因此，我们提出了一种基于光流图像的多尺度联合特征网络，用于微表情识别。首先，我们生成一个反映细微面部运动信息的光流图像。然后将光流图像馈入多尺度联合网络中，以进行特征提取和分类。拟议的联合要素模块（JFM）集成了来自不同层的要素，这有利于捕获具有不同幅度的微表达特征。为了提高模型的识别能力，我们还采用了将三个JFM的特征预测结果与骨干网络融合的策略。我们的实验结果表明，在三个基准数据集（SMIC，CASME II和SAMM）和组合数据集（3DB）上，我们的方法优于最新方法。