CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术研究
Enhanced Local Gradient Order Features and Discriminant Analysis for Face Recognition
Ren, Chuan-Xian1; Lei, Zhen2,3; Dai, Dao-Qing1; Li, Stan Z.2,3
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
2016-11-01
Volume46Issue:11Pages:2656-2669
SubtypeArticle
AbstractRobust 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.
KeywordDiscontinuity Image Gradient Order Features Sparse Representation Subspace Learning
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TCYB.2015.2484356
WOS KeywordDIMENSIONALITY REDUCTION ; SPARSE REPRESENTATION ; VERIFICATION ; REGULARIZATION ; CLASSIFIER ; EIGENFACES ; DESCRIPTOR ; PATTERNS ; MODELS ; POSE
Indexed BySCI
Language英语
Funding OrganizationNational 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)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000386227000023
Citation statistics
Cited Times:14[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13329
Collection模式识别国家重点实验室_生物识别与安全技术研究
Affiliation1.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
Recommended Citation
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.
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