基于深度学习和证据理论的表情识别模型

表情识别是在人脸检测基础之上的更进一步研究，是计算机视觉领域的一个重要研究方向。将研究的目标定位于基于微视频的表情自动识别，研究在大数据环境下，如何使用深度学习技术来辅助和促进表情识别技术的发展。针对表情智能识别过程中存在的一些关键性技术难题，设计了一个全自动表情识别模型。该模型结合深度自编码网络和自注意力机制，构建了一个人脸表情特征自动提取子模型，然后结合证据理论对多特征分类结果进行有效融合。实验结果表明，该模型能显著提升表情识别的准确度，具有重要的理论意义和研究价值。

An expression recognition model based on deep learning and evidence theory

Facial expression recognition is a further research based on face detection, which is an important research direction in the field of computer vision. The goal of the research is to automatically recognize facial expressions based on micro video and study how to use deep learning technology to assist and promote the development of facial expression recognition technology in a big data environment. A fully automated expression recognition model has been designed to address some of the key technical challenges in the expression intelligence recognition process. The model combines a deep auto-encoding network and a self-attention mechanism to construct a sub-model for automatic extraction of facial expression features, and then the evidence theory is used to fuse the results of multi-feature classification. Experimental results show that the model can significantly improve the accuracy of expression recognition, which has important theoretical significance and research value.