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  • 3D face recognition under partial occlusions using radial strings

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    Accepted Manuscript (AM)
    Author(s)
    Yu, Xun
    Gao, Yongsheng
    Zhou, Jun
    Griffith University Author(s)
    Gao, Yongsheng
    Zhou, Jun
    Yu, Alex
    Year published
    2016
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    Abstract
    3D face recognition with partial occlusions is a highly challenging problem. In this paper, we propose a novel radial string representation and matching approach to recognize 3D facial scans in the presence of partial occlusions. Here we encode 3D facial surfaces into an indexed collection of radial strings emanating from the nosetips and Dynamic Programming (DP) is then used to measure the similarity between two radial strings. In order to address the recognition problems with partial occlusions, a partial matching mechanism is established in our approach that effectively eliminates those occluded parts and finds the most ...
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    3D face recognition with partial occlusions is a highly challenging problem. In this paper, we propose a novel radial string representation and matching approach to recognize 3D facial scans in the presence of partial occlusions. Here we encode 3D facial surfaces into an indexed collection of radial strings emanating from the nosetips and Dynamic Programming (DP) is then used to measure the similarity between two radial strings. In order to address the recognition problems with partial occlusions, a partial matching mechanism is established in our approach that effectively eliminates those occluded parts and finds the most discriminative parts during the matching process. Experimental results on the Bosphorus database demonstrate that the proposed approach yields superior performance on partially occluded data.
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    Conference Title
    2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
    Volume
    2016-August
    DOI
    https://doi.org/10.1109/ICIP.2016.7532913
    Copyright Statement
    © 2016 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/10072/124190
    Collection
    • Conference outputs

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