3D Convolutional Neural Networks for Facial Expression Classification

被引：0

作者：

Sun, Wenyun ^{[1
]}

Zhao, Haitao ^{[2
]}

Jin, Zhong ^{[1
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China

[2] East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China

来源：

COMPUTER VISION - ACCV 2016 WORKSHOPS, PT I | 2017年 / 10116卷

关键词：

RECOGNITION; FEATURES;

D O I：

10.1007/978-3-319-54407-6_35

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, the general rules of designing 3D Convolutional Neural Networks are discussed. Four specific networks are designed for facial expression classification problem. Decisions of the four networks are fused together. The single networks and the ensemble network are evaluated on the extended Cohn-Kanade dataset, achieve accuracies of 92.31% and 96.15%. The performance outperform the state-of-the-art. A reusable open source project called 4DCNN is released. Based on this project, implementing 3D Convolutional Neural Networks for specific problems will be convenient.

引用

页码：528 / 543

页数：16

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