CASIA OpenIR  > 智能感知与计算
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)
Conference NameAsian Conference on Pattern Recognition (ACPR)
Conference Date2017.11.26-2017.11.29
Conference PlaceNanjing, China
AbstractFace 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.
Document Type会议论文
Corresponding AuthorHe R(赫然)
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhihe Lu,Zhihang Li,Jie Cao,et al. Recent Progress of Face Image Synthesis[C],2017.
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