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. © 2020 IEEE.