Multi-view frontal face image generation: A survey

被引:61
|
作者
Ning, Xin [1 ,2 ,3 ]
Nan, Fangzhe [2 ,3 ]
Xu, Shaohui [2 ,3 ]
Yu, Lina [1 ]
Zhang, Liping [1 ,2 ,3 ]
机构
[1] Chinese Acad Sci, Inst Semicond, Beijing 100083, Peoples R China
[2] Wave Grp, Cognit Comp Technol Joint Lab, Beijing, Peoples R China
[3] Shenzhen Wave Kingdom Co Ltd, Shenzhen, Peoples R China
来源
基金
中国国家自然科学基金;
关键词
3D model; deep learning; face frontalization; hybrid model; POSE NORMALIZATION; RECOGNITION; DATABASE;
D O I
10.1002/cpe.6147
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Face images from different perspectives reduce the accuracy of face recognition, and the generation of frontal face images is an important research topic in the field of face recognition. To understand the development of frontal face generation models and grasp the current research hotspots and trends, existing methods based on 3D models, deep learning, and hybrid models are summarized, and the current commonly used face generation methods are introduced. Dataset, and compare the performance of existing models through experiments. The purpose of this paper is to fundamentally understand the advantages of existing frontal face generation, sort out the key issues of such generation, and look toward future development trends.
引用
收藏
页数:18
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