OPT OpenIR  > 光谱成像技术研究室
Facial feature point detection: A comprehensive survey
Wang, Nannan1; Gao, Xinbo2; Tao, Dacheng3; Yang, Heng4; Li, Xuelong5
Source PublicationNEUROCOMPUTING
Contribution Rank5

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.

KeywordDeep Learning Face Alignment Facial Feature Point Detection Facial Landmark Localization
Indexed BySCI ; EI
WOS IDWOS:000418370200006
EI Accession Number20172403766296
Citation statistics
Cited Times:30[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorGao, Xinbo
Affiliation1.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
Recommended Citation
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.
Files in This Item:
File Name/Size DocType Version Access License
Facial feature point(1544KB)期刊论文出版稿限制开放CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Nannan]'s Articles
[Gao, Xinbo]'s Articles
[Tao, Dacheng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Nannan]'s Articles
[Gao, Xinbo]'s Articles
[Tao, Dacheng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Nannan]'s Articles
[Gao, Xinbo]'s Articles
[Tao, Dacheng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.