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Facial Expression Recognition through Self-supervised Learning for Predicting Face Image Sequence

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(9), pp.41-47
  • DOI : 10.9708/jksci.2022.27.09.041
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : August 2, 2022
  • Accepted : September 6, 2022
  • Published : September 30, 2022

Yeo Chan Yoon 1 Soo Kyun Kim 1

1제주대학교

Accredited

ABSTRACT

In this paper, we propose a new and simple self-supervised learning method that predicts the middle image of a face image sequence for automatic expression recognition. Automatic facial expression recognition can achieve high performance through deep learning methods, however, generally requires a expensive large data set. The size of the data set and the performance of the algorithm are tend to be proportional. The proposed method learns latent deep representation of a face through self-supervised learning using an existing dataset without constructing an additional dataset. Then it transfers the learned parameter to new facial expression reorganization model for improving the performance of automatic expression recognition. The proposed method showed high performance improvement for two datasets, CK+ and AFEW 8.0, and showed that the proposed method can achieve a great effect.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.