@inproceedings{f2cc17aa53b3403c924818f5808b0623,
title = "Emotion Enhancement for Facial Images Using GAN",
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.",
keywords = "CNN, Deep Learning, Facial Expression Recognition (FER), GAN",
author = "Kim, {Jun Hwa} and Won, {Chee Sun}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020 ; Conference date: 01-11-2020 Through 03-11-2020",
year = "2020",
month = nov,
day = "1",
doi = "10.1109/ICCE-Asia49877.2020.9277349",
language = "English",
series = "2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020",
address = "United States",
}