Unsupervised Domain Adaptation for Facial Expression Recognition Using Generative Adversarial Networks

Computational Intelligence and Neuroscience
Xiaoqing WangYubo Ni

Abstract

In the facial expression recognition task, a good-performing convolutional neural network (CNN) model trained on one dataset (source dataset) usually performs poorly on another dataset (target dataset). This is because the feature distribution of the same emotion varies in different datasets. To improve the cross-dataset accuracy of the CNN model, we introduce an unsupervised domain adaptation method, which is especially suitable for unlabelled small target dataset. In order to solve the problem of lack of samples from the target dataset, we train a generative adversarial network (GAN) on the target dataset and use the GAN generated samples to fine-tune the model pretrained on the source dataset. In the process of fine-tuning, we give the unlabelled GAN generated samples distributed pseudolabels dynamically according to the current prediction probabilities. Our method can be easily applied to any existing convolutional neural networks (CNN). We demonstrate the effectiveness of our method on four facial expression recognition datasets with two CNN structures and obtain inspiring results.

References

Oct 1, 1987·Journal of Personality and Social Psychology·P EkmanP E Ricci-Bitti
Sep 22, 2016·Proceedings of the National Academy of Sciences of the United States of America·Steven K EsserDharmendra S Modha

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Citations

May 1, 2021·Sensors·Shervin MinaeeAmirali Abdolrashidi
Jul 3, 2021·Mathematical Biosciences and Engineering : MBE·R Nandhini Abiram, P M Durai Raj Vincent

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Software Mentioned

DCGAN
DPL
JAFFE
WGAN
VGG11
InfoGAN
GAN
Alexnet
Pseudolabel
LSRO

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