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Search : [ keyword: Image-to-Image Translation ] (2)

RDID-GAN: Reconstructing a De-identified Image Dataset to Generate Effective Learning Data

Wonseok Oh, Kangmin Bae, Yuseok Bae

http://doi.org/10.5626/JOK.2021.48.12.1329

Recently, CCTVs have been installed to prevent or handle various social problems, and there are many efforts to develop visual surveillance systems based on deep neural networks. However, the datasets collected from CCTVs are inappropriate to train models due to privacy issues. Therefore, in this paper, we proposed RDID-GAN, an effective dataset de-identification method that can remove privacy issues and negative effects raised by modifying the dataset using a de-identification procedure. RDID-GAN focuses on a de-identified region to produce competitive results by adopting the attention module. Through the experiments, we compared RDID-GAN and the conventional image-to-image translation models qualitatively and quantitatively.

Facial Emotion Recognition Data Augmentation using Generative Adversarial Network

Jinyong Kim, Geunsik Jo

http://doi.org/10.5626/JOK.2021.48.4.398

The facial emotion recognition field of computer vision has recently been identified to demonstrate meaningful results through various neural networks. However, the major datasets of facial emotion recognition have the problem of “class imbalance,” which is a factor that degrades the accuracy of deep learning models. Therefore, numerous studies have been actively conducted to solve the problem of class imbalance. In this paper, we propose “RDGAN,” a facial emotion recognition data augmentation model that uses a GAN to solve the class imbalance of the FER2013 and RAF_single that are used as facial emotion recognition datasets. RDGAN is a network that generates images suitable for classes by adding expression discriminators based on the image-to-image translation model between the existing images as compared to the prevailing studies. The dataset augmented with RDGAN showed an average performance improvement of 4.805%p and 0.857%p in FER2013 and RAF_single, respectively, compared to the dataset without data augmentation.


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