NDLI: Face recognition by decision fusion of two-dimensional linear discriminant analysis and local binary pattern

Content Provider	Springer Nature Link
Author	Wang, Qicong Wang, Binbin Hao, Xinjie Chen, Lisheng Cui, Jingmin Ji, Rongrong Lei, Yunqi
Copyright Year	2016
Abstract	To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases (the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases). By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the two-dimensional linear discriminant analysis (2DLDA) and local binary pattern (LBP) are proposed in this paper to heighten the recognition rates. In addition, partitioning a face non-uniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms.
Starting Page	1118
Ending Page	1129
Page Count	12
File Format	PDF
ISSN	20952228
Journal	Frontiers of Computer Science in China
Volume Number	10
Issue Number	6
e-ISSN	20952236
Language	English
Publisher	Higher Education Press
Publisher Date	2016-06-09
Publisher Institution	Chinese Universities
Publisher Place	Beijing
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	face recognition global feature local feature linear discriminant analysis local binary pattern decision fusion Computer Science
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Science

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