摘要: 面部表情识别是人机交互研究的热点，广泛应用于各方面，涉及信息学、心理学等，在教学评价方面具有很好的研究前景。区别于一般的静态人脸表情识别，微表情识别不但需要提取图像中人脸表情形变的空间特征信息，还需要考虑到连续图像序列的时间运动信息。本文基于面部动态表情序列，针对静态表情缺少时间信息等问题，将空间特征与时间特征融合，利用神经网络在图像分类领域良好的特征，对需要进行细节分析的表情序列进行处理，提出基于S-LRCN(Separate Long-term recurrent Convolutional Networks)的微表情识别方法。首先选取微表情数据集提取面部图像序列，引入迁移学习的方法，通过预训练的卷积神经网络模型提取表情帧的空间特征，降低网络训练中过拟合的危险，并将视频序列的提取特征输入长短期记忆网络(Long Short-Team Memory, LSTM)处理时域特征。最后建立学习者表情序列小型数据库，将该方法用于辅助教学评价。
Research on Micro Expression Recognition Algorithm Based on S-LRCN
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Abstract: With the rapid development of machine learning and deep neural network and the popularization of intelligent devices, face recognition technology has developed rapidly. At present, the accuracy of face recognition has exceeded that of human eyes. At the same time, the software and hardware conditions of large-scale popularization have been available, and the application fields are widely distributed. As an important part of face recognition technology, facial expression recognition has been widely concerned in the fields of artificial intelligence, security, automation, medical treatment and driving in recent years. Expression recognition, as a hot topic of human-computer interaction, involves Informatics, psychology and so on, and has a good research prospect in teaching evaluation. Among them, micro-expression is a kind of short-lived facial expression that human beings unconsciously make when trying to hide some emotion, which has great research significance. Different from the general static facial expression recognition, micro expression recognition not only needs to extract the spatial feature information of facial expression deformation in the image, but also needs to consider the time motion information of the continuous image sequence. In this paper, for the problem of static expression features lacking time information, which cannot fully reflect the subtle changes in expression, etc., facial dynamic expression sequences are used to fuse spatial features and temporal features, and use neural networks to provide good features in the field of image classification. Expression sequences are processed, and a micro-expression recognition method based on S-LRCN is proposed. Firstly, the micro expression data set is selected to extract the facial image sequence, and the transfer learning method is introduced to extract the spatial features of the expression frame through the convolution neural network model of pre training, so as to reduce the risk of over fitting in the network training, and the extracted features of the video sequence are input into LSTM to process the time-domain features. Finally, a small database of learners' expression sequences is established, and the method is used to assist teaching evaluation.