Two-dimensional quaternion sparse principle component analysis
Xiao X.; Zhou Y.
Source PublicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
AbstractMotivated 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 inherent-1y takes the advantage of 2DPCA in preserving the structure of two-dimensional data, as well as the strength of quaternion-s in representing color images holistically. Benefited from the sparsity constraints, 2DQSPCA is robust for occlusions. Experiments demonstrate the superior performance of 2DQSP-CA on color face recognition, especially with occlusions.
Keyword2DPCA Color face recognition Quaternion Sparse
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Document TypeConference paper
CollectionUniversity of Macau
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Xiao X.,Zhou Y.. Two-dimensional quaternion sparse principle component analysis[C],2018:1528-1532.
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