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  • Derivative Variation Pattern for Illumination-Invariant Image Representation

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    Accepted Manuscript (AM)
    Author(s)
    Tavakolian, Mohammad
    Hajati, Farshid
    Mian, Ajmal S
    Gao, Yongsheng
    Gheisari, Soheila
    Griffith University Author(s)
    Gao, Yongsheng
    Year published
    2013
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    Abstract
    This paper presents a novel image descriptor called Derivative Variation Pattern (DVP) and its application to face and palmprint recognition. DVP captures image variations in both the frequency and the spatial domains. The effects of uncontrolled illumination are compensated in the frequency domain by discarding the illumination affected frequencies. Image pixels are encoded as binary patterns based on the higher-order spatial derivatives computed in the spatial domain. The proposed descriptor was evaluated on the Extended Yale-B and FERET face databases, and the PolyU palmprint database. Experimental results demonstrate the ...
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    This paper presents a novel image descriptor called Derivative Variation Pattern (DVP) and its application to face and palmprint recognition. DVP captures image variations in both the frequency and the spatial domains. The effects of uncontrolled illumination are compensated in the frequency domain by discarding the illumination affected frequencies. Image pixels are encoded as binary patterns based on the higher-order spatial derivatives computed in the spatial domain. The proposed descriptor was evaluated on the Extended Yale-B and FERET face databases, and the PolyU palmprint database. Experimental results demonstrate the effectiveness of the DVP descriptor in both the face and the palmprint recognition tasks under uncontrolled illuminations.
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    Conference Title
    2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013)
    DOI
    https://doi.org/10.1109/ICIP.2013.6738867
    Copyright Statement
    © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Computer vision
    Publication URI
    http://hdl.handle.net/10072/58767
    Collection
    • Conference outputs

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