基于深度学习的微表情识别研究

发布时间：2018-03-26 08:03

  本文选题：微表情识别　切入点：3-d卷积神经网络　出处：《温州大学》2017年硕士论文

【摘要】：短时间人们表情的变化,也叫微表情,心理学在这方面的研究已经很早就开始了,近年来,有关利用机器学习的方法来对微表情进行研究的学者越来越多,是当前研究的一个热门方向。在这几年的微表情识别的研究中,有几个团队为微表情研究建立了数据集供其他研究者使用,并且提出了一些算法来解决微表情识别的问题。自从2012年卷积神经网络在ImageNet的比赛上取得了重大突破以后,基于卷积神经网络的深度学习方法在图像识别领域取得了越来越好的结果。本人在做微表情识别的研究中主要做了两种方法:1、第一种方法是基于卷积神经网络的,主要采用了基于光流的3-d CNN的网络结构,将最简单的VggNet网络加上微表情视频时间序列的信息构成3维的信息输入,同时将X和Y方向的3-d光流场信息与原始灰度的3-d信息经过三个通道的3-d CNN处理,最后将三部分信息组合并分类。2、第二种方法是利用了积分投影和LSTM来对微表情进行识别。现有的微表情识别研究主要是利用基于局部二值模式(LBP)改进的算法并结合支持向量机(SVM)来识别。最近,积分投影开始应用于人脸识别领域。长短时记忆网络(LSTM)作为循环神经网络,可以用来处理时序数据。因此提出了结合积分投影和LSTM的模型(LSTM-IP),在最新的微表情数据库CASME Ⅱ中进行实验。通过积分投影得到水平和垂直投影向量作为LSTM输入并分类,同时采用防止过拟合技术。实.验结果表明,LSTM-IP算法模型取得了比以前的方法更好的精度。
[Abstract]:The changes in people's expressions in a short period of time, also known as microexpressions, have long been the subject of psychological research. In recent years, more and more scholars have studied microexpressions using machine learning methods. In recent years of research on microfacial expression recognition, several teams have created data sets for microfacial expression studies for use by other researchers. And some algorithms are put forward to solve the problem of microfacial expression recognition. Since 2012, the convolutional neural network has made a great breakthrough in the ImageNet competition. The depth learning method based on convolution neural network has obtained more and more good results in the field of image recognition. In the research of micro expression recognition, I have mainly done two methods: 1. The first method is based on convolutional neural network. The network structure of 3-d CNN based on optical flow is mainly adopted. The simplest VggNet network and the information of microfacial video time series are used to form 3D information input. At the same time, the 3-d optical flow field information in X and Y directions and the 3-d information in the original gray level are processed by 3-d CNN with three channels. Finally, the three parts of information are combined and classified. 2. The second method is to use integral projection and LSTM to recognize micro-expression. The existing research on micro-expression recognition is mainly based on the improved algorithm based on local binary pattern. Support vector machine (SVM) to identify. Recently, Integral projection has been applied to face recognition. LSTM (long and short time memory Network) is used as a cyclic neural network. It can be used to deal with time series data. Therefore, a model combining integral projection and LSTM is proposed to carry out experiments in the latest microfacial expression database CASME 鈪，

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