中国科学院重庆绿色智能技术研究院机构知识库

QQ客服 官方微博 反馈留言

	Robust facial landmark detection using mixture of discriminative visibility-aware models
	Liu, Yanfei; Zhou, Xiangdong; Li, Yuanqian; Shao, Xiaohu; Zhou, Xi
	2016
摘要	Facial landmark detection is a fundamental process included in many face analysis tasks. However, it faces great challenges for the facial images to be detected usually containing large variations, which will deteriorate the detection precision. Among such variations, partial occlusions and pose variations take great effects. In this study, the authors present a mixture of discriminative visibility-aware models (MDVMs) for facial landmark detection, to improve the generalisation ability of the model to occlusion and pose variation. By adopting different structure constrains for different poses as well as selecting different appearance model for the occluded parts, the MDVMs method can efficiently address the problem of partial occlusion and pose variation. Experiment results demonstrate that their proposed MDVMs method outperforms the well-known template-based methods, and can obtain much more accurate and robust facial landmarks detection results under both occlusions and pose variations.
DOI	10.1049/iet-ipr.2015.0699
发表期刊	IET IMAGE PROCESSING
ISSN	1751-9659
卷号	10期号:10页码:733-741
通讯作者	Zhou, X (reprint author), Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Automated Reasoning & Cognit Key Lab Chongqing, 266 Fangzheng Ave,Shuitu Hitech Ind Pk, Chongqing, Peoples R China.
收录类别	SCI
WOS记录号	WOS:000385563600007
语种	英语