Deep Convolutional Neural Network for Facial Expression Recognition

被引：7
|
作者：
Zhai, Yikui [1 ]
Liu, Jian [1 ]
Zeng, Junying [1 ]
Piuri, Vincenzo [2 ]
Scotti, Fabio [2 ]
Ying, Zilu [1 ]
Xu, Ying [1 ]
Gan, Junying [1 ]
机构：
[1] Wuyi Univ, Sch Elect & Informat Engineer, Jiangmen 529020, Peoples R China
[2] Univ Milan, Dept Comp Sci, I-26013 Crema, Italy
来源：
IMAGE AND GRAPHICS (ICIG 2017), PT I | 2017年 / 10666卷
关键词：
Facial expression recognition (FER); Deep convolutional neural network; Transfer learning;
D O I：
10.1007/978-3-319-71607-7_19
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, a deep convolutional neural network model and the method of transfer learning are used to solve the problems of facial expression recognition (FER). Firstly, the method of transfer learning was adopted and face recognition net was transferred into facial expression recognition net. And then, in order to enhance the classification ability of our proposed model, a modified Softmax loss function (Softmax-MSE) and a double activation layer (DAL) are proposed. We performed our experiment on enhanced SFEW2.0 dataset and FER2013 dataset. The experiments have achieved overall classification accuracy of 48.5% and 59.1% respectively, which achieved the state-of-art performance.
引用
收藏
页码：211 / 223
页数：13
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