Emotion Enhancement for Facial Images Using GAN

Jun Hwa Kim, Chee Sun Won

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Labeled images play an important role for training convolutional neural networks (CNN). In particular, training CNNs for facial emotion classification, the publicly available datasets suffer from noisy labels and inter-class imbalance problem. In this paper, we adopt a Generative Adversarial Network (GAN) to alleviate both noisy labeling and inter-class imbalance problems. Specifically, the noisy labelled images are identified by cross-checking the classified results with two fine-tuned CNNs and their facial emotions are strengthened by a GAN. Also, some of the neutral emotion images are transformed into minor emotion classes to solve the imbalance problem.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728161648
DOIs
StatePublished - 1 Nov 2020
Event2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020 - Seoul, Korea, Republic of
Duration: 1 Nov 20203 Nov 2020

Publication series

Name2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020

Conference

Conference2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020
Country/TerritoryKorea, Republic of
CitySeoul
Period1/11/203/11/20

Keywords

  • CNN
  • Deep Learning
  • Facial Expression Recognition (FER)
  • GAN

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