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    Face recognition based on manifold constrained joint sparse sensing with K-SVD

    Access Status
    Fulltext not available
    Authors
    Liu, J.
    Liu, Wan-Quan
    Ma, S.
    Lu, Chong
    Xiu, X.
    Pathirage, N.
    Li, Ling
    Chen, G.
    Zeng, W.
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Liu, J. and Liu, W. and Ma, S. and Lu, C. and Xiu, X. and Pathirage, N. and Li, L. et al. 2018. Face recognition based on manifold constrained joint sparse sensing with K-SVD. Multimedia Tools and Applications: pp. 1-21.
    Source Title
    Multimedia Tools and Applications
    DOI
    10.1007/s11042-018-6071-9
    ISSN
    1380-7501
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/69140
    Collection
    • Curtin Research Publications
    Abstract

    © 2018 Springer Science+Business Media, LLC, part of Springer Nature Face recognition based on Sparse representation idea has recently become an important research topic in computer vision community. However, the dictionary learning process in most of the existing approaches suffers from the perturbations brought by the variations of the input samples, since the consistence of the learned dictionaries from similar input samples based on K-SVD are not well addressed in the existing literature. In this paper, we will propose a novel technique for dictionary learning based on K-SVD to address the consistence issue. In particular, the proposed method embeds the manifold constraints into a standard dictionary learning framework based on k-SVD and force the optimization process to satisfy the structure preservation requirement. Therefore, this new approach can consistently integrate the manifold constraints during the optimization process, and it can contribute a better solution which is robust to the variance of the input samples. Extensive experiments on several popular face databases show a consistent performance improvement in comparison to some related state-of-the-art algorithms.

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