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  • A 3D polygonal line chains matching method for face recognition

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    Author(s)
    Yu, Xun
    Gao, Yongsheng
    Zhou, Jun
    Griffith University Author(s)
    Gao, Yongsheng
    Zhou, Jun
    Yu, Alex
    Year published
    2013
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    Abstract
    In this paper, a novel 3D polygonal line chains matching method is proposed. Different from traditional method that use points and meshes to represent and match 3D shapes, our method represents 3D surfaces using 3D polygonal line chains generated from ridge and valley curves. Then a 3D polygonal line segment Hausdorff distance measure is developed to compute the similarity between two 3D surfaces. This representation, along with the distance metric, can effectively harness structural and spatial information on a 3D surface. The added information can provide more and better discrimination power for object recognition. It ...
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    In this paper, a novel 3D polygonal line chains matching method is proposed. Different from traditional method that use points and meshes to represent and match 3D shapes, our method represents 3D surfaces using 3D polygonal line chains generated from ridge and valley curves. Then a 3D polygonal line segment Hausdorff distance measure is developed to compute the similarity between two 3D surfaces. This representation, along with the distance metric, can effectively harness structural and spatial information on a 3D surface. The added information can provide more and better discrimination power for object recognition. It strengthens and improves the matching process of similar 3D objects such as 3D faces. Experiments on FRGC v2 database leads to a rank one recognition rate of 96.1%.
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    Conference Title
    2013 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES & APPLICATIONS (DICTA)
    Publisher URI
    https://staff.itee.uq.edu.au/lovell/aprs/dicta13/
    DOI
    https://doi.org/10.1109/DICTA.2013.6691471
    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/10072/57262
    Collection
    • Conference outputs

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