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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
ISSN0925-2312
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
DOI10.1016/j.neucom.2017.05.013
收录类别SCI
语种英语
WOS记录号WOS:000418370200006
引用统计
被引频次:4[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/30824
专题光学影像学习与分析中心
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.
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