An incrementally cascaded broad learning framework to facial landmark tracking

doi:10.1016/j.neucom.2020.05.025

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 机器人应用与理论组

	An incrementally cascaded broad learning framework to facial landmark tracking
	Liu, Caifeng 3; Feng, Lin 1; Guo, Shuai 1; Wang, Huibing 2; Liu, Shenglan 1; Qiao, Hong 4
刊名	NEUROCOMPUTING
	2020-10-14
卷号	410 页码:125-137
关键词	Facial landmark tracking Face alignment Cascade regression Incremental learning
ISSN号	0925-2312
DOI	10.1016/j.neucom.2020.05.025
通讯作者	Feng, Lin(fenglin@dlut.edu.cn)
英文摘要	Facial landmark tracking often adopts static models to generically fit per frame of video. This is considered inappropriate since such models ignore the informative correlation between previous and current frames. Moreover, most of these methods fail to balance the speed and accuracy for video-based facial landmark tracking simultaneously. In this paper, we propose an efficient online framework for video-based face alignment, named Incrementally Cascaded Broad Learning framework (ICBL). ICBL aims to continuously enhance the prediction capability of tracking model for sequential data. It is capable of learning the spatial appearance on specific-person statistics from continuous facial frames and using such knowledge to incrementally tune a cascade of regressors in parallel. To achieve this goal, we approximate the facial shape space by sampling from a dynamic distribution which is continuously updated by person-specific statistics from the tracked facial frames. This dramatically facilitates cascade regression to incrementally update all cascade-regressors in parallel, thus allowing a fast update of the whole model. Furthermore, we successfully incorporate both the linear and non-linear mappings into our parallel cascade framework and introduce Broad Learning (BL) algorithm as a solution for them simultaneously. Experimental results on the most popular and large-scale benchmark for facial landmark tracking show highly competitive performance of proposed ICBL in comparisons with the state-of-the-arts. The code of our ICBL framework has been available from https://github.com/CaifengLiu/Facial-landmark-tracking-by-ICBL. (C) 2020 Elsevier B.V. All rights reserved.
资助项目	National Key Research and Development Program of China[2017YFB1300200] ; National Key Research and Development Program of China[2017YFB1300203] ; National Natural Science Fund of China[61972064] ; National Natural Science Fund of China[61672130] ; National Natural Science Fund of China[61602082] ; National Natural Science Fund of China[61627808] ; National Natural Science Fund of China[91648205] ; LiaoNing Revitalization Talents Program[XLYC1806006] ; Fundamental Research Funds for the Central Universities[DUT19RC(3)012] ; Fundamental Research Funds for the Central Universities[DUT17RC(3)071] ; development of science and technology of Guangdong province special fund project[2016B090910001]
WOS关键词	FACE ALIGNMENT ; NETWORK
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000579799300012
资助机构	National Key Research and Development Program of China ; National Natural Science Fund of China ; LiaoNing Revitalization Talents Program ; Fundamental Research Funds for the Central Universities ; development of science and technology of Guangdong province special fund project
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42138]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
通讯作者	Feng, Lin
作者单位	1.Dalian Univ Technol, Sch Innovat & Entrepreneurship, Dalian 116024, Peoples R China 2.Dalian Maritime Univ, Coll Informat Sci & Technol, Dalian 116024, Peoples R China 3.Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Liu, Caifeng,Feng, Lin,Guo, Shuai,et al. An incrementally cascaded broad learning framework to facial landmark tracking[J]. NEUROCOMPUTING,2020,410:125-137.
APA	Liu, Caifeng,Feng, Lin,Guo, Shuai,Wang, Huibing,Liu, Shenglan,&Qiao, Hong.(2020).An incrementally cascaded broad learning framework to facial landmark tracking.NEUROCOMPUTING,410,125-137.
MLA	Liu, Caifeng,et al."An incrementally cascaded broad learning framework to facial landmark tracking".NEUROCOMPUTING 410(2020):125-137.