Facial feature point detection: A comprehensive survey | |
Wang, Nannan1; Gao, Xinbo2; Tao, Dacheng3; Yang, Heng4; Li, Xuelong5 | |
作者部门 | 光学影像学习与分析中心 |
2018-01-31 | |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
卷号 | 275页码:50-65 |
产权排序 | 5 |
摘要 | This paper presents a comprehensive survey of facial feature point detection with the assistance of abundant manually labeled images. Facial feature point detection favors many applications such as face recognition, animation, tracking, hallucination, expression analysis and 3D face modeling. Existing methods are categorized into two primary categories according to whether there is the need of a parametric shape model: parametric shape model-based methods and nonparametric shape model-based methods. Parametric shape model-based methods are further divided into two secondary classes according to their appearance models: local part model-based methods (e.g. constrained local model) and holistic model-based methods (e.g. active appearance model). Nonparametric shape model-based methods are divided into several groups according to their model construction process: exemplar-based methods, graphical model-based methods, cascaded regression-based methods, and deep learning based methods. Though significant progress has been made, facial feature point detection is still limited in its success by wild and real-world conditions: large variations across poses, expressions, illuminations, and occlusions. A comparative illustration and analysis of representative methods provides us a holistic understanding and deep insight into facial feature point detection, which also motivates us to further explore more promising future schemes. (c) 2017 Elsevier B.V. All rights reserved. |
关键词 | Deep Learning Face Alignment Facial Feature Point Detection Facial Landmark Localization |
DOI | 10.1016/j.neucom.2017.05.013 |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000418370200006 |
EI入藏号 | 20172403766296 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/30824 |
专题 | 光谱成像技术研究室 |
通讯作者 | Gao, Xinbo |
作者单位 | 1.Xidian Univ, Sch Telecommun Engn, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China; 2.Xidian Univ, Sch Elect Engn, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China; 3.Univ Sydney, Sch Informat Technol, UBTech Sydney Artificial Intelligence Inst, J12 Cleveland St, Darlington, NSW 2008, Australia; 4.ULSee Inc, Hangzhou 310016, Zhejiang, Peoples R China; 5.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Nannan,Gao, Xinbo,Tao, Dacheng,et al. Facial feature point detection: A comprehensive survey[J]. NEUROCOMPUTING,2018,275:50-65. |
APA | Wang, Nannan,Gao, Xinbo,Tao, Dacheng,Yang, Heng,&Li, Xuelong.(2018).Facial feature point detection: A comprehensive survey.NEUROCOMPUTING,275,50-65. |
MLA | Wang, Nannan,et al."Facial feature point detection: A comprehensive survey".NEUROCOMPUTING 275(2018):50-65. |
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 | ||
Facial feature point(1544KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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