A joint cascaded framework for simultaneous eye detection and eye state estimation

doi:10.1016/j.patcog.2017.01.023

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	A joint cascaded framework for simultaneous eye detection and eye state estimation
	Gou, Chao1,3,4 ; Wu, Yue2 ; Wang, Kang 2; Wang, Kunfeng1 ; Wang, Fei-Yue1,3 ; Ji, Qiang 2
发表期刊	PATTERN RECOGNITION
	2017-07-01
卷号	67 期号:1 页码:23-31
文章类型	Article
摘要	Eye detection and eye state (close/open) estimation are important for a wide range of applications, including iris recognition, visual interaction and driver fatigue detection. Current work typically performs eye detection first, followed by eye state estimation by a separate classifier. Such an approach fails to capture the interactions between eye location and its state. In this paper, we propose a method for simultaneous eye detection and eye state estimation. Based on a cascade regression framework, our method iteratively estimates the location of the eye and the probability of the eye being occluded by eyelid. At each iteration of cascaded regression, image features from the eye center as well as contextual image features from eyelid and eye corners are jointly used to estimate the eye position and openness probability. Using the eye openness probability, the most likely eye state can be estimated. Since it requires large number of facial images with labeled eye related landmarks, we propose to combine the real and synthetic images for training. It further improves the performance by utilizing this learning-by-synthesis method. Evaluations of our method on benchmark databases such as BioID and Gi4E database as well as on real world driving videos demonstrate its superior performance comparing to state-of-the-art methods for both eye detection and eye state estimation. (C) 2017 Elsevier Ltd. All rights reserved.
关键词	Eye Detection Eye State Estimation Learning-by-synthesis Cascade Regression Framework
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.patcog.2017.01.023
关键词[WOS]	PUPIL LOCALIZATION ; FEATURES ; ROBUST
收录类别	SCI
语种	英语
项目资助者	University of Chinese Academy of Sciences (UCAS) ; UCAS ; RPI ; National Science Foundation(1145152) ; National Natural Science Foundation of China(61304200 ; 61533019)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000399520700003
引用统计	被引频次：72[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/14484
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Gou, Chao
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Rensselaer Polytech Inst, Dept Elect Comp & Syst Engn, Troy, NY 12180 USA 3.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Gou, Chao,Wu, Yue,Wang, Kang,et al. A joint cascaded framework for simultaneous eye detection and eye state estimation[J]. PATTERN RECOGNITION,2017,67(1):23-31.
APA	Gou, Chao,Wu, Yue,Wang, Kang,Wang, Kunfeng,Wang, Fei-Yue,&Ji, Qiang.(2017).A joint cascaded framework for simultaneous eye detection and eye state estimation.PATTERN RECOGNITION,67(1),23-31.
MLA	Gou, Chao,et al."A joint cascaded framework for simultaneous eye detection and eye state estimation".PATTERN RECOGNITION 67.1(2017):23-31.

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