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A New Facial Expression Recognition Scheme Based on Parallel Double Channel Convolutional Neural Network

  • D. T. Li
  • F. JiangEmail author
  • Y. B. Qin
Conference paper
  • 270 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12034)

Abstract

The conventional deep convolutional neural networks in facial expression recognition are confronted with the training inefficiency due to many layered structure with a large number of parameters, in order to cope with this challenge, in this work, an improved convolutional neural network—with parallel double channels, termed as PDC-CNN, is proposed. Within this model, we can get two different sized feature maps for the input image, and the two different sized feature maps are combined for the final recognition and judgment. In addition, in order to prevent over-fitting, we replace the traditional RuLU activation function with the Maxout model in the fully connected layer to optimize the performance of the network. We have trained and tested the new model on JAFFE dataset. Experimental results show that the proposed method can achieve 83% recognition rate, in comparison with the linear SVM, AlexNet and LeNet-5, the recognition rate of this method is improved by 14%–28%.

Keywords

Facial expression recognition Parallel Double channel Convolutional neural network 

Notes

Acknowledgements

This work is partially supported by National Natural Science Foundation of China (61762018), the Guangxi 100 Youth Talent Program (F-KA16016) and the Colleges and Universities Key Laboratory of Intelligent Integrated Automation, Guilin University of Electronic Technology, China (GXZDSY2016-03), the research fund of Guangxi Key Lab of Multi-source Information Mining & Security (18-A-02-02), Natural Science Foundation of Guangxi (2018GXNSFAA281310).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Guangxi Key Lab of Multi-source Information Mining and SecurityCollege of Electronic Engineering, Guangxi Normal UniversityGuilinChina
  2. 2.Faculty of Engineering and ITUniversity of Technology SydneyUltimoAustralia

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