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Combining Data-Driven and Model-Driven Methods for Robust Facial Landmark Detection
Zhang, Hongwen1,2; Li, Qi1; Sun, Zhenan1,2; Liu, Yunfan1
Source PublicationIEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
2018-10-01
Volume13Issue:10Pages:2409-2422
SubtypeArticle
Abstract
; Facial landmark detection is an important yet challenging task for real-world computer vision applications. This paper proposes an effective and robust approach for facial landmark detection by combining data-and model-driven methods. First, a fully convolutional network (FCN) is trained to compute response maps of all facial landmark points. Such a data-driven method could make full use of holistic information in a facial image for global estimation of facial landmarks. After that, the maximum points in the response maps are fitted with a pre-trained point distribution model (PDM) to generate the initial facial shape. This model-driven method is able to correct the inaccurate locations of outliers by considering the shape prior information. Finally, a weighted version of regularized landmark mean-shift (RLMS) is employed to fine-tune the facial shape iteratively. This estimation-correction-tuning process perfectly combines the advantages of the global robustness of the data-driven method (FCN), outlier correction capability of the model-driven method (PDM), and non-parametric optimization of RLMS. Results of extensive experiments demonstrate that our approach achieves state-of-the-art performances on challenging data sets, including 300W, AFLW, AFW, and COFW. The proposed method is able to produce satisfying detection results on face images with exaggerated expressions, large head poses, and partial occlusions.
KeywordFacial Landmark Detection Face Alignment Fully Convolutional Network Point Distribution Model Weighted Regularized Mean Shift
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TIFS.2018.2800901
WOS KeywordACTIVE APPEARANCE MODELS ; FACE ALIGNMENT ; LOCALIZATION
Indexed BySCI
Language英语
Funding OrganizationNational Key Research and Development Program of China(2016YFB1001000) ; National Natural Science Foundation of China(61573360 ; 61427811 ; 61702513)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000431895700001
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20762
Collection智能感知与计算研究中心
Affiliation1.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Ctr Res Intelligent Percept & Comp, Inst Automat,Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Hongwen,Li, Qi,Sun, Zhenan,et al. Combining Data-Driven and Model-Driven Methods for Robust Facial Landmark Detection[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2018,13(10):2409-2422.
APA Zhang, Hongwen,Li, Qi,Sun, Zhenan,&Liu, Yunfan.(2018).Combining Data-Driven and Model-Driven Methods for Robust Facial Landmark Detection.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,13(10),2409-2422.
MLA Zhang, Hongwen,et al."Combining Data-Driven and Model-Driven Methods for Robust Facial Landmark Detection".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 13.10(2018):2409-2422.
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