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    A unified tensor framework for face recognition

    Access Status
    Fulltext not available
    Authors
    Rana, Santu
    Liu, Wan-quan
    Lazarescu, Mihai
    Venkatesh, Svetha
    Date
    2009
    Type
    Journal Article
    
    Metadata
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    Citation
    Rana, Santu and Liu, Wanquan and Lazarescu, Mihai and Venkatesh, Svetha. 2009. A unified tensor framework for face recognition. Pattern recognition. 42 (11): pp. 2850-2862.
    Source Title
    Pattern recognition
    DOI
    10.1016/j.patcog.2009.03.018
    ISSN
    00313203
    Faculty
    School of Science and Computing
    Department of Computing
    Faculty of Science and Engineering
    Remarks

    The link to the journal’s home page is: http://www.elsevier.com/wps/find/journaldescription.cws_home/328/description#description. Copyright © 2009 Elsevier B.V. All rights reserved

    URI
    http://hdl.handle.net/20.500.11937/38703
    Collection
    • Curtin Research Publications
    Abstract

    In this paper we propose a new optimization framework that unites some of the existing tensor based methods for face recognition on a common mathematical basis. Tensor based approaches rely on the ability to decompose an image into its constituent factors (i.e. person, lighting, viewpoint, etc.) and then utilizing these factor spaces for recognition. We first develop a multilinear optimization problem relating an image to its constituent factors and then develop our framework by formulating a set of strategies that can be followed to solve this optimization problem. The novelty of our research is that the proposed framework offers an effective methodology for explicit non-empirical comparison of the different tensor methods as well as providing a way to determine the applicability of these methods in respect to different recognition scenarios. Importantly, the framework allows the comparative analysis on the basis of quality of solutions offered by these methods. Our theoretical contribution has been validated by extensive experimental results using four benchmark datasets which we present along with a detailed discussion.

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