Locality-constrained framework for face alignment

doi:10.1007/s11704-018-6617-z

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Locality-constrained framework for face alignment
	Zhao, Xiaowei 1; Chen, Xilin 3; Chai, Xiujuan 3; Shan, Shiguang 3; Zhang, Jie 2,3; Kan, Meina 3
刊名	FRONTIERS OF COMPUTER SCIENCE
	2019-08-01
卷号	13 期号:4 页码:789-801
关键词	locality-constrained AAM locality-constrained DFM face alignment sparsity-regularization
ISSN号	2095-2228
DOI	10.1007/s11704-018-6617-z
英文摘要	Although the conventional active appearance model (AAM) has achieved some success for face alignment, it still suffers from the generalization problem when be applied to unseen subjects and images. To deal with the generalization problem of AAM, we first reformulate the original AAM as sparsity-regularized AAM, which can achieve more compact/better shape and appearance priors by selecting nearest neighbors as the bases of the shape and appearance model. To speed up the fitting procedure, the sparsity in sparsity-regularized AAM is approximated by using the locality (i.e., K-nearest neighbor), and thus inducing the locality-constrained active appearance-model (LC-AAM). The LC-AAM solves a constrained AAM-like fitting problem with the K-nearest neighbors as the bases of shape and appearance model. To alleviate the adverse influence of inaccurate K-nearest neighbor results, the locality constraint is further embedded in the discriminative fitting method denoted as LC-DFM, which can find better K-nearest neighbor results by employing shape-indexed feature, and can also tolerate some inaccurate neighbors benefited from the regression model rather than the generative model in AAM. Extensive experiments on several datasets demonstrate that our methods outperform the state-of-the-arts in both detection accuracy and generalization ability.
资助项目	National Natural Science Foundation of China[61650202] ; National Natural Science Foundation of China[61402443] ; National Natural Science Foundation of China[61672496] ; Strategic Priority Research Program of the CAS[XDB02070004]
WOS研究方向	Computer Science
语种	英语
出版者	HIGHER EDUCATION PRESS
WOS记录号	WOS:000469220400008
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/4205]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Shan, Shiguang
作者单位	1.Alibaba Grp, Hangzhou 311121, Zhejiang, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Xiaowei,Chen, Xilin,Chai, Xiujuan,et al. Locality-constrained framework for face alignment[J]. FRONTIERS OF COMPUTER SCIENCE,2019,13(4):789-801.
APA	Zhao, Xiaowei,Chen, Xilin,Chai, Xiujuan,Shan, Shiguang,Zhang, Jie,&Kan, Meina.(2019).Locality-constrained framework for face alignment.FRONTIERS OF COMPUTER SCIENCE,13(4),789-801.
MLA	Zhao, Xiaowei,et al."Locality-constrained framework for face alignment".FRONTIERS OF COMPUTER SCIENCE 13.4(2019):789-801.