RTCRelief-F: an effective clustering and ordering-based ensemble pruning algorithm for facial expression recognition

  • Danyang Li
  • Guihua Wen
  • Zhi Hou
  • Eryang Huan
  • Yang Hu
  • Huihui Li
Regular Paper
  • 32 Downloads

Abstract

Ensemble pruning is effective for improving the accuracy of expression recognition. This paper proposes a novel ensemble pruning algorithm called RTCRelief-F and applies it to facial expression recognition. RTCRelief-F uses a novel classifier-representation method that accounts for the interaction among classifiers and bases the classifier selection upon not only diversity but accuracy. Additionally, for the first time, RTCRelief-F, applies the Relief-F algorithm to evaluate the classifiers’ ability and resets the fusion order. Finally, the combination of a clustering-based ensemble pruning method and the ordering-based ensemble pruning method can both alleviate the dependence of a selected subset S on the adopted clustering strategies and guarantee the diversity of the selected subset S. The experimental results show that this method outperforms or competes with the original ensemble and some major state-of-the-art results on the data sets Fer2013, JAFFE, and CK\(+\).

Keywords

Facial expression recognition Ensemble pruning Convolutional neural network Relative transformation Hierarchical clustering Relief-F 

Notes

Acknowledgements

This study was supported by a China National Science Foundation under Grants (60973083, 61273363), Science and Technology Planning Projects of Guangdong Province (2014A010103009, 2015A020217002), and Guangzhou Science and Technology Planning Project (201504291154480)

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Danyang Li
    • 1
  • Guihua Wen
    • 1
  • Zhi Hou
    • 1
  • Eryang Huan
    • 1
  • Yang Hu
    • 1
  • Huihui Li
    • 1
  1. 1.School of Computer Science and EngineeringSouth China University of TechnologyPanyu District, Guangzhou CityChina

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