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Quaternion Sparse Discriminant Analysis for Color Face Recognition
Xiaolin Xiao; Yicong Zhou
Conference NameIEEE International Conference on Multimedia and Expo (ICME)
Source Publication2018 IEEE International Conference on Multimedia and Expo (ICME)
Conference Date23-27 July 2018
Conference PlaceSan Diego, CA, USA

To reduce feature dimensions while obtaining robust classifi- cation, in this paper, we propose quaternion sparse discriminant analysis (QSDA) for color face recognition. QSDA is formulated as a quaternion sparse regression-type model. It employs the quaternion algebra to provide an elegant and holistic way to represent color face images. The succeeding operations are directly applied to two-dimensional quaternion matrices, and hence QSDA is computationally efficient and well preserves the spatial structure of color face images. Benefited from sparsity constraints, QSDA is robust for classification. An alternating minimization algorithm is designed to solve QSDA. Experimental results demonstrate the effectiveness of QSDA for color face recognition, especially for partially occluded color face images

KeywordQsda Feature Extraction Color Face Recognition Partial Occlusion
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Document TypeConference paper
CollectionFaculty of Science and Technology
AffiliationDepartment of Computer and Information Science, University of Macau, Macau, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Xiaolin Xiao,Yicong Zhou. Quaternion Sparse Discriminant Analysis for Color Face Recognition[C],2018.
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