基于多任务学习和知识图谱的面部表情识别

针对面部表情分类的模型中参数较复杂、识别准确率较低的问题，提出了一种基于知识图谱辅助识别的多任务学习算法模型（MLAM），该模型由基于深度学习的识别模块与知识图谱嵌入模块两部分构成。首先从输入的数据中提取潜在的人脸局部表情特征，通过知识图谱实现局部表情和个体的复杂交互；然后在MLAM 模型中设计一个交叉压缩单元，关联这两个独立模块，自动学习局部表情和实体特征的高级交互，并在这两个任务之间传递交叉知识转移；最后，在FER2013和CK+的数据集上对比了同类算法，实验结果表明，该模型在上述数据集上分别得到了0.69和0.99的识别率，提高了面部表情识别准确率。

Facial Expression Recognition Based on Multi-Task Learning and Knowledge Graph

Aiming at the complicated parameters and low recognition accuracy rate in classification models of facial expression, this paper proposes a multi-task learning algorithm model (MLAM) based on knowledge graph assisted recognition，which consists of recognition module and knowledge graph embedding module based on deep learning. First, the potential facial expression features from the input data were extracted, and the complex interaction between the local expression and the individual through the knowledge graph was realized; then a cross & compress unit was designed in the MLAM, these two independent modules were associated, and the local expression and the advanced interaction of entity features were automatically learned and cross-knowledge between these two tasks was transferred. Finally, similar algorithms were compared on the FER2013 and CK+ data sets. The results showed that the model obtains recognition rates of 68.85% and 99.16% respectively on the above data sets, which improves the accuracy of facial expression recognition.