| Recent Progress of Face Image Synthesis | |
Zhihe Lu(卢治合)2,3,4 ; Zhihang Li(李志航)1,2,4; Jie Cao(曹杰)1,2,4; Ran He(赫然)1,2,3,4; Zhenan Sun(孙哲南)1,2,4; He R(赫然)
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| 2017-12 | |
| 会议名称 | Asian Conference on Pattern Recognition (ACPR) |
| 会议日期 | 2017.11.26-2017.11.29 |
| 会议地点 | Nanjing, China |
| 摘要 | Face synthesis has been a fascinating yet challenging problem in computer vision and machine learning. Its main research effort is to design algorithms to generate photorealistic face images via given semantic domain. It has been a crucial prepossessing step of main-stream face recognition approaches and an excellent test of AI ability to use complicated probability distributions. In this paper, we provide a comprehensive review of typical face synthesis works that involve traditional methods as well as advanced deep learning approaches. Particularly, Generative Adversarial Net (GAN) is highlighted to generate photo-realistic and identity preserving results. Furthermore, the public available databases and evaluation metrics are introduced in details. We end the review with discussing unsolved difficulties and promising directions for future research. |
| 文献类型 | 会议论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/19896 |
| 专题 | 智能感知与计算研究中心 |
| 通讯作者 | He R(赫然) |
| 作者单位 | 1.National Laboratory of Pattern Recognition, CASIA 2.Center for Research on Intelligent Perception and Computing, CASIA 3.Center for Excellence in Brain Science and Intelligence Technology, CAS 4.University of Chinese Academy of Sciences, Beijing |
| 第一作者单位 | 模式识别国家重点实验室 |
| 推荐引用方式 GB/T 7714 | Zhihe Lu,Zhihang Li,Jie Cao,et al. Recent Progress of Face Image Synthesis[C],2017. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Recent Progress of F（197KB） | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 | ||
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