Enhanced Local Gradient Order Features and Discriminant Analysis for Face Recognition

doi:10.1109/TCYB.2015.2484356

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 生物识别与安全技术研究中心

	Enhanced Local Gradient Order Features and Discriminant Analysis for Face Recognition
	Ren, Chuan-Xian 1; Lei, Zhen2,3 ; Dai, Dao-Qing 1; Li, Stan Z.2,3
刊名	IEEE TRANSACTIONS ON CYBERNETICS
	2016-11-01
卷号	46 期号:11 页码:2656-2669
关键词	Discontinuity Image Gradient Order Features Sparse Representation Subspace Learning
DOI	10.1109/TCYB.2015.2484356
文献子类	Article
英文摘要	Robust descriptor-based subspace learning with complex data is an active topic in pattern analysis and machine intelligence. A few researches concentrate the optimal design on feature representation and metric learning. However, traditionally used features of single-type, e.g., image gradient orientations (IGOs), are deficient to characterize the complete variations in robust and discriminant subspace learning. Meanwhile, discontinuity in edge alignment and feature match are not been carefully treated in the literature. In this paper, local order constrained IGOs are exploited to generate robust features. As the difference-based filters explicitly consider the local contrasts within neighboring pixel points, the proposed features enhance the local textures and the order-based coding ability, thus discover intrinsic structure of facial images further. The multimodal features are automatically fused in the most discriminant subspace. The utilization of adaptive interaction function suppresses outliers in each dimension for robust similarity measurement and discriminant analysis. The sparsity-driven regression model is modified to adapt the classification issue of the compact feature representation. Extensive experiments are conducted by using some benchmark face data sets, e.g., of controlled and uncontrolled environments, to evaluate our new algorithm.
WOS关键词	DIMENSIONALITY REDUCTION ; SPARSE REPRESENTATION ; VERIFICATION ; REGULARIZATION ; CLASSIFIER ; EIGENFACES ; DESCRIPTOR ; PATTERNS ; MODELS ; POSE
WOS研究方向	Computer Science
语种	英语
WOS记录号	WOS:000386227000023
资助机构	National Science Foundation of China(11171354 ; Ministry of Education of China(SRFDP-20120171120007 ; Natural Science Foundation of Guangdong Province(S2013020012796) ; Fundamental Research Funds for the Central Universities(13lgpy26) ; Open Project Program of the National Laboratory of Pattern Recognition ; 61203248 ; 20120171110016) ; 61375033 ; 61572536)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/13329]
专题	自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
作者单位	1.Sun Yat Sen Univ, Sch Math & Computat Sci, Intelligent Data Ctr, Guangzhou 510275, Guangdong, Peoples R China 2.Chinese Acad Sci, Ctr Biometr & Secur Res, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Ren, Chuan-Xian,Lei, Zhen,Dai, Dao-Qing,et al. Enhanced Local Gradient Order Features and Discriminant Analysis for Face Recognition[J]. IEEE TRANSACTIONS ON CYBERNETICS,2016,46(11):2656-2669.
APA	Ren, Chuan-Xian,Lei, Zhen,Dai, Dao-Qing,&Li, Stan Z..(2016).Enhanced Local Gradient Order Features and Discriminant Analysis for Face Recognition.IEEE TRANSACTIONS ON CYBERNETICS,46(11),2656-2669.
MLA	Ren, Chuan-Xian,et al."Enhanced Local Gradient Order Features and Discriminant Analysis for Face Recognition".IEEE TRANSACTIONS ON CYBERNETICS 46.11(2016):2656-2669.