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  • Heterogeneous face recognition via Grassmannian based nearest subspace search

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    TianPUB3173.pdf (1.507Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Tian, Yuan
    Yan, Cheng
    Bai, Xiao
    Zhou, Jun
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2017
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    Abstract
    Heterogeneous face recognition involves matching faces in different image modalities, such as near infrared images to visible images or sketch images to photos. This challenging task has attracted increasing attention in recent years. This paper presents, for the first time, a subspace based method to tackle the problem of face recognition between visible images (VIS) and near infrared (NIR) images. Subspace is used to extract essential attributes from VIS and NIR images. We adopt Grassmannian radial basis function (RBF) kernel to keep the relationship between subspaces, and use kernel canonical correlation analysis (KCCA) ...
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    Heterogeneous face recognition involves matching faces in different image modalities, such as near infrared images to visible images or sketch images to photos. This challenging task has attracted increasing attention in recent years. This paper presents, for the first time, a subspace based method to tackle the problem of face recognition between visible images (VIS) and near infrared (NIR) images. Subspace is used to extract essential attributes from VIS and NIR images. We adopt Grassmannian radial basis function (RBF) kernel to keep the relationship between subspaces, and use kernel canonical correlation analysis (KCCA) to handle correlation mapping between VIS and NIR domains. After mapping both VIS and NIR images to the common space, the heterogeneous face recognition problem can be easily completed by the nearest search. We evaluate the proposed method on the CASIA NIR-VIS 2.0 dataset. The experimental results demonstrate that our method is very effective for NIR-VIS face recognition.
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    Conference Title
    2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
    Volume
    2017-September
    DOI
    https://doi.org/10.1109/ICIP.2017.8296447
    Copyright Statement
    © 2017 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
    Image processing
    Publication URI
    http://hdl.handle.net/10072/377028
    Collection
    • Conference outputs

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