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  • 3D Face Recognition using Topographic High-order Derivatives

    Author(s)
    Cheraghian, Ali
    Hajati, Farshid
    Mian, Ajmal S
    Gao, Yongsheng
    Gheisari, Soheila
    Griffith University Author(s)
    Gao, Yongsheng
    Hajati, Farshid
    Year published
    2013
    Metadata
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    Abstract
    This paper presents a novel feature, Topographic High-order Derivatives (THD) for 3D face recognition. THD is based on the high-order micro-pattern information extracted from face topography maps. Face topography maps are partitioned into polar sectors, and THDs are computed using directional highorder derivatives within the sectors. Local features are extracted by encoding directional high-order derivatives within polar neighborhoods. To evaluate the proposed method, we use Bosphorus and FRGC 3D face databases which include pose and expression changes. The performance of the proposed method is higher compared to the ...
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    This paper presents a novel feature, Topographic High-order Derivatives (THD) for 3D face recognition. THD is based on the high-order micro-pattern information extracted from face topography maps. Face topography maps are partitioned into polar sectors, and THDs are computed using directional highorder derivatives within the sectors. Local features are extracted by encoding directional high-order derivatives within polar neighborhoods. To evaluate the proposed method, we use Bosphorus and FRGC 3D face databases which include pose and expression changes. The performance of the proposed method is higher compared to the state-of-the-art benchmark approaches in 3D face recognition.
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    Conference Title
    2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013)
    DOI
    https://doi.org/10.1109/ICIP.2013.6738764
    Subject
    Computer vision
    Publication URI
    http://hdl.handle.net/10072/58766
    Collection
    • Conference outputs

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