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Face recognition by decision fusion of two-dimensional linear discriminant analysis and local binary pattern |
Qicong WANG,Binbin WANG,Xinjie HAO,Lisheng CHEN,Jingmin CUI,Rongrong JI,Yunqi LEI( ) |
Department of Computer Science, School of Information Science and Engineering, Xiamen University, Xiamen 361005, China |
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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 twodimensional linear discriminant analysis (2DLDA) and local binary pattern (LBP) are proposed in this paper to heighten the recognition rates. In addition, partitioning a face nonuniformly 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.
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Keywords
face recognition
global feature
local feature
linear discriminant analysis
local binary pattern
decision fusion
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Corresponding Author(s):
Yunqi LEI
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Just Accepted Date: 22 March 2016
Online First Date: 12 June 2016
Issue Date: 11 October 2016
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