Combining Data-Driven and Model-Driven Methods for Robust Facial Landmark Detection

doi:10.1109/TIFS.2018.2800901

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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, Yunfan 1
刊名	IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
	2018-10-01
卷号	13 期号:10 页码:2409-2422
关键词	Facial Landmark Detection Face Alignment Fully Convolutional Network Point Distribution Model Weighted Regularized Mean Shift
DOI	10.1109/TIFS.2018.2800901
文献子类	Article
英文摘要	; 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.
WOS关键词	ACTIVE APPEARANCE MODELS ; FACE ALIGNMENT ; LOCALIZATION
WOS研究方向	Computer Science ; Engineering
语种	英语
WOS记录号	WOS:000431895700001
资助机构	National Key Research and Development Program of China(2016YFB1001000) ; National Natural Science Foundation of China(61573360 ; 61427811 ; 61702513)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/20762]
专题	自动化研究所_智能感知与计算研究中心
作者单位	1.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
推荐引用方式 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.