Generative Adversarial Network for Multi Facial Attributes Translation
Recently, Generative Adversarial Network (GAN) based approaches are applied in facial attribute translation. However, many tasks, i.e. multi facial attributes translation and background invariance, are not well handled in the literature. In this paper, we propose a novel GAN-based method that aims t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9537764/ |
| _version_ | 1798003337289793536 |
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| author | Jun Liu Xiaoyang Liu Yanjun Feng |
| author_facet | Jun Liu Xiaoyang Liu Yanjun Feng |
| author_sort | Jun Liu |
| collection | DOAJ |
| description | Recently, Generative Adversarial Network (GAN) based approaches are applied in facial attribute translation. However, many tasks, i.e. multi facial attributes translation and background invariance, are not well handled in the literature. In this paper, we propose a novel GAN-based method that aims to get the target image that performs better within modifying one or more facial attributes in a single model. The model generator learns multi-points by inputting a re-coded transfer vector, ensuring the single model could learn multiple attributes simultaneously. It also optimizes the cycle loss to enhance the efficiency of transferring multi attributes. Moreover, the method uses the adaptive parameter to improve the calculation method of the loss function of the residual image. The results are also compared with the StarGAN v2, which is the current state-of-the-art model to prove the effectiveness and advancedness. Experiments show that our method has a satisfactory performance in multi facial attributes translation. |
| first_indexed | 2024-04-11T12:05:58Z |
| format | Article |
| id | doaj.art-3befb201b1844a9382250653c2687d6f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T12:05:58Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3befb201b1844a9382250653c2687d6f2022-12-22T04:24:43ZengIEEEIEEE Access2169-35362021-01-01912937512938410.1109/ACCESS.2021.31128959537764Generative Adversarial Network for Multi Facial Attributes TranslationJun Liu0https://orcid.org/0000-0001-5756-0074Xiaoyang Liu1Yanjun Feng2School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, ChinaSchool of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, ChinaSchool of Information Science and Engineering, Shenyang Ligong University, Shenyang, ChinaRecently, Generative Adversarial Network (GAN) based approaches are applied in facial attribute translation. However, many tasks, i.e. multi facial attributes translation and background invariance, are not well handled in the literature. In this paper, we propose a novel GAN-based method that aims to get the target image that performs better within modifying one or more facial attributes in a single model. The model generator learns multi-points by inputting a re-coded transfer vector, ensuring the single model could learn multiple attributes simultaneously. It also optimizes the cycle loss to enhance the efficiency of transferring multi attributes. Moreover, the method uses the adaptive parameter to improve the calculation method of the loss function of the residual image. The results are also compared with the StarGAN v2, which is the current state-of-the-art model to prove the effectiveness and advancedness. Experiments show that our method has a satisfactory performance in multi facial attributes translation.https://ieeexplore.ieee.org/document/9537764/GANdeep learningfacial attribute translationloss optimization |
| spellingShingle | Jun Liu Xiaoyang Liu Yanjun Feng Generative Adversarial Network for Multi Facial Attributes Translation IEEE Access GAN deep learning facial attribute translation loss optimization |
| title | Generative Adversarial Network for Multi Facial Attributes Translation |
| title_full | Generative Adversarial Network for Multi Facial Attributes Translation |
| title_fullStr | Generative Adversarial Network for Multi Facial Attributes Translation |
| title_full_unstemmed | Generative Adversarial Network for Multi Facial Attributes Translation |
| title_short | Generative Adversarial Network for Multi Facial Attributes Translation |
| title_sort | generative adversarial network for multi facial attributes translation |
| topic | GAN deep learning facial attribute translation loss optimization |
| url | https://ieeexplore.ieee.org/document/9537764/ |
| work_keys_str_mv | AT junliu generativeadversarialnetworkformultifacialattributestranslation AT xiaoyangliu generativeadversarialnetworkformultifacialattributestranslation AT yanjunfeng generativeadversarialnetworkformultifacialattributestranslation |