![](/images/graphics-bg.png)
Unsupervised Domain Adaptation for Facial Expression Recognition Using Generative Adversarial Networks
Joint Authors
Wang, Xiaoqing
Wang, Xiangjun
Ni, Yubo
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-09
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Xiaoqing& Wang, Xiangjun& Ni, Yubo. 2018. Unsupervised Domain Adaptation for Facial Expression Recognition Using Generative Adversarial Networks. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1130830
Modern Language Association (MLA)
Wang, Xiaoqing…[et al.]. Unsupervised Domain Adaptation for Facial Expression Recognition Using Generative Adversarial Networks. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1130830
American Medical Association (AMA)
Wang, Xiaoqing& Wang, Xiangjun& Ni, Yubo. Unsupervised Domain Adaptation for Facial Expression Recognition Using Generative Adversarial Networks. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1130830
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130830