基于深度学习的表情识别方法研究
[Abstract]:Facial expression is an indispensable way of human communication. Through the study of facial expression, we can explore the psychological state of human beings, and then fully understand the behavior intention of human beings. Deep learning is a feature learning method. It can solve the problems of speech processing, computer vision, natural language processing and so on by transforming data into higher level and more abstract expression through some simple nonlinear models. In this paper, deep learning is used to solve some problems in expression recognition, and it is verified by experiments. The main contents of this paper are as follows: 1. In this paper, many kinds of deep learning models are studied, which can be divided into deep convolution neural network, depth belief network, depth Boltzmann machine, stacking automatic encoder and recurrent neural network. They have different algorithms and different fields of application. Therefore, choosing the appropriate deep learning model is the key to solve the problem of expression recognition. Through comparison and demonstration, it is found that the special local connection and weight sharing mechanism of deep convolution neural network can solve the problems of large feature dimension and difficult computation in facial expression recognition. Therefore, in this paper, the deep convolution neural network is chosen as the depth learning model of this paper. 2. In order to solve the problem that feature extraction in static expression recognition will lose the original feature information of image, this paper proposes to use depth convolution neural network in depth learning model to realize expression feature extraction. Because the deep convolution neural network avoids the complex pre-processing of the image, it can directly input the original image. It can extract the features through the joint action of convolution and pooling, and it does not need man-made feature extraction, and the network is easy to train. The generalization performance of the fully connected neural network is better than that of the fully connected neural network, so the deep convolution neural network is applied to static expression recognition. In order to solve the problems of poor anti-jamming, slow computing speed and poor real-time performance in dynamic expression recognition, a method of dynamic expression feature extraction based on deep convolution neural network is proposed in this paper. Because the real-time acquired dynamic facial expression sequence from the dynamic expression recognition system is different from the static facial expression recognition, it is necessary for the system to store and recognize the acquired face in real-time. In order to solve this problem, the Haar classifier is used for face detection, and then the deep convolution neural network is introduced to construct the essential features of the image, extract the expression features, and finally use the Softmax classifier to realize the expression classification. 4. In order to improve the nonlinear expression ability of the deep convolution neural network and achieve better expression feature extraction, the network structure is improved and the deep continuous convolution neural network is used to realize the expression recognition. In this paper, the deep convolution neural network is improved, and the idea that the multi-layer small-scale convolution replaces the single-layer large-scale convolution is introduced, that is, the two-layer continuous convolution layer is used to replace the single-layer convolution layer, and the nonlinear expression ability of the network is improved. Then the activation function and parameter optimization method of the network are adjusted to improve the expression feature fitting ability of the network.
【学位授予单位】:长春工业大学
【学位级别】:硕士
【学位授予年份】:2017
【分类号】:TP391.41
【参考文献】
相关期刊论文 前10条
1 张友梅;张伟;;基于数据融合的表情识别[J];四川大学学报(工程科学版);2016年06期
2 何小飞;邹峥嵘;陶超;张佳兴;;联合显著性和多层卷积神经网络的高分影像场景分类[J];测绘学报;2016年09期
3 杨格兰;邓晓军;刘琮;;基于深度时空域卷积神经网络的表情识别模型[J];中南大学学报(自然科学版);2016年07期
4 刘帅师;程曦;郭文燕;陈奇;;深度学习方法研究新进展[J];智能系统学报;2016年05期
5 杨雨浓;房鼎益;王洪;;一种基于混合深度置信模型的面部表情识别方法[J];西南大学学报(自然科学版);2016年06期
6 陈耀丹;王连明;;基于卷积神经网络的人脸识别方法[J];东北师大学报(自然科学版);2016年02期
7 孙晓;潘汀;任福继;;基于ROI-KNN卷积神经网络的面部表情识别[J];自动化学报;2016年06期
8 王伟凝;王励;赵明权;蔡成加;师婷婷;徐向民;;基于并行深度卷积神经网络的图像美感分类[J];自动化学报;2016年06期
9 马晓;张番栋;封举富;;基于深度学习特征的稀疏表示的人脸识别方法[J];智能系统学报;2016年03期
10 牛连强;陈向震;张胜男;王琪辉;;深度连续卷积神经网络模型构建与性能分析[J];沈阳工业大学学报;2016年06期
相关博士学位论文 前1条
1 万川;基于动态序列图像的人脸表情识别系统理论与方法研究[D];吉林大学;2013年
相关硕士学位论文 前10条
1 产文涛;基于卷积神经网络的人脸表情和性别识别[D];安徽大学;2016年
2 刘旷;基于卷积网络集成的面部表情识别方法[D];浙江大学;2016年
3 陈向震;基于深度学习的人脸表情识别算法研究[D];沈阳工业大学;2016年
4 曹宁;基于静态图像的人脸表情识别算法研究[D];西安科技大学;2015年
5 池燕玲;基于深度学习的人脸识别方法的研究[D];福建师范大学;2015年
6 施徐敢;基于深度学习的人脸表情识别[D];浙江理工大学;2015年
7 汪济民;基于卷积神经网络的人脸检测和性别识别研究[D];南京理工大学;2015年
8 叶浪;基于卷积神经网络的人脸识别研究[D];东南大学;2015年
9 高旭;基于动态序列图像的人脸表情特征提取与识别[D];吉林大学;2014年
10 刘银华;LBP和深度信念网络在非限制条件下人脸识别研究[D];五邑大学;2014年
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