|本期目录/Table of Contents|

[1]林志坤,许建龙,包晓安.基于STGAN的人脸属性编辑改进模型[J].浙江理工大学学报,2023,49-50(自科三):285-292.
 LIN Zhikun,XU Jianlong,BAO Xiaoan.Improved model of face attribute editing based on STGAN[J].Journal of Zhejiang Sci-Tech University,2023,49-50(自科三):285-292.
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基于STGAN的人脸属性编辑改进模型()
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浙江理工大学学报[ISSN:1673-3851/CN:33-1338/TS]

卷:
第49-50卷
期数:
2023年自科第三期
页码:
285-292
栏目:
出版日期:
2023-05-31

文章信息/Info

Title:
Improved model of face attribute editing based on STGAN
文章编号:
1673-3851 (2023) 05-0285-08
作者:
林志坤许建龙包晓安
浙江理工大学,a.信息科学与工程学院;b.计算机科学与技术学院,杭州 310018
Author(s):
LIN Zhikun XU Jianlong BAO Xiao′an
a.School of Information Science and Engineering; b.School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
关键词:
生成对抗网络人脸编辑重构图像潜码解耦
分类号:
TP391
文献标志码:
A
摘要:
人脸属性编辑在美颜APP和娱乐领域有重要应用,但现有方法存在生成图像质量不高、属性编辑不够准确等问题,为此提出了一种基于选择传输生成对抗网络(Selective transfer generative adversarial networks, STGAN)的人脸属性编辑改进模型。运用潜码解耦合思想,将潜码分解为内容潜码和风格潜码单独操作,提高源域图像和目标域图像的内容编码一致性,从而提高属性编辑准确率;同时运用像素级重构损失和潜码重构损失,在总损失函数中加入像素级限制和潜码重构限制,通过互补作用提高生成图像质量。在CelebA人脸数据集和季节数据集上进行实验,该模型相比当前人脸属性编辑主流模型在定性结果和定量指标上均有提高,其中峰值信噪比和结构相似性相比STGAN模型分别提高了6.06%和1.58%。这说明该改进模型能够有效提高人脸属性编辑的性能,满足美颜APP和娱乐领域的需求。

参考文献/References:

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6 Wang T C, Liu M Y, Zhu J Y, et al. High - resolution image synthesis and semantic manipulation with conditional GANs C ]∥ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 8798 - 8807.
7 Zhu J Y, Park T, Isola P, et al. Unpaired image - to - image translation using cycle - consistent adversarial networks C ]∥ Proceedings of the IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2242 - 2251.
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备注/Memo

备注/Memo:
收稿日期: 2022-09-01
网络出版日期:2023-01-16
基金项目: 浙江省重点研发计划项目(2020C03094)
作者简介: 林志坤(1997-),男,浙江温州人,硕士研究生,主要从事深度学习、计算机视觉等方面的研究
通信作者: 许建龙,E-mail:xujianlong126@126.com
更新日期/Last Update: 2023-09-08