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3D Convolutional Neural Networks for Facial Expression Classification

  • Wenyun Sun
  • Haitao Zhao
  • Zhong JinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10116)

Abstract

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.

Keywords

Convolutional Neural Network Facial Expression Recognition Human Action Recognition Fiducial Point Facial Action Code System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.School of Information Science and EngineeringEast China University of Science and TechnologyShanghaiChina

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