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Two-Dimensional Quaternion Sparse Principal Component Analysis
Xiaolin Xiao; Yicong Zhou
2018-09-13
Conference Name2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Source Publication2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages1528-1532
Conference Date15-20 April 2018
Conference PlaceCalgary, AB, Canada
Abstract

Motivated by the facts that, (1), the spatial structure of images and the correlation among color channels are important for color face recognition, and (2), natural face images may be occluded, in this work, we propose two-dimensional quaternion sparse principle component analysis (2DQSPCA) to extract features for color face recognition. 2DQSPCA inherently takes the advantage of 2DPCA in preserving the structure of two-dimensional data, as well as the strength of quaternions in representing color images holistically. Benefited from the sparsity constraints, 2DQSPCA is robust for occlusions. Experiments demonstrate the superior performance of 2DQSPCA on color face recognition, especially with occlusions

Keyword2dpca Quaternion Sparse Color Face Recognition
DOIhttp://doi.org/10.1109/ICASSP.2018.8462668
Indexed BySCI
Language英语
WOS Research AreaAcoustics ; Engineering
WOS SubjectAcoustics ; Engineering, Electrical & Electronic
WOS IDWOS:000446384601143
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Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationDepartment of Computer and Information Science, University of Macau
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Xiaolin Xiao,Yicong Zhou. Two-Dimensional Quaternion Sparse Principal Component Analysis[C],2018:1528-1532.
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