3D face recognition using topographic high-order derivatives

A. Cheraghian, F. Hajati, Ajmal Mian, G. Yongsheng, S. Gheisari

    Research output: Chapter in Book/Conference paperConference paper

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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 high-order 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.
    Original languageEnglish
    Title of host publicationProceedings of 20th IEEE International Conference on Image Processing (ICIP), 2013
    Place of PublicationAustralia
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages3705-3709
    ISBN (Print)9781479923410
    DOIs
    Publication statusPublished - 2013
    Event2013 IEEE International Conference on Image Processing - Melbourne, Australia
    Duration: 15 Sep 201318 Sep 2013

    Conference

    Conference2013 IEEE International Conference on Image Processing
    CountryAustralia
    CityMelbourne
    Period15/09/1318/09/13

    Fingerprint

    Face recognition
    Derivatives
    Topography

    Cite this

    Cheraghian, A., Hajati, F., Mian, A., Yongsheng, G., & Gheisari, S. (2013). 3D face recognition using topographic high-order derivatives. In Proceedings of 20th IEEE International Conference on Image Processing (ICIP), 2013 (pp. 3705-3709). Australia: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICIP.2013.6738764
    Cheraghian, A. ; Hajati, F. ; Mian, Ajmal ; Yongsheng, G. ; Gheisari, S. / 3D face recognition using topographic high-order derivatives. Proceedings of 20th IEEE International Conference on Image Processing (ICIP), 2013. Australia : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 3705-3709
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    title = "3D face recognition using topographic high-order derivatives",
    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 high-order 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.",
    author = "A. Cheraghian and F. Hajati and Ajmal Mian and G. Yongsheng and S. Gheisari",
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    Cheraghian, A, Hajati, F, Mian, A, Yongsheng, G & Gheisari, S 2013, 3D face recognition using topographic high-order derivatives. in Proceedings of 20th IEEE International Conference on Image Processing (ICIP), 2013. IEEE, Institute of Electrical and Electronics Engineers, Australia, pp. 3705-3709, 2013 IEEE International Conference on Image Processing, Melbourne, Australia, 15/09/13. https://doi.org/10.1109/ICIP.2013.6738764

    3D face recognition using topographic high-order derivatives. / Cheraghian, A.; Hajati, F.; Mian, Ajmal; Yongsheng, G.; Gheisari, S.

    Proceedings of 20th IEEE International Conference on Image Processing (ICIP), 2013. Australia : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 3705-3709.

    Research output: Chapter in Book/Conference paperConference paper

    TY - GEN

    T1 - 3D face recognition using topographic high-order derivatives

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    AU - Mian, Ajmal

    AU - Yongsheng, G.

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    N2 - 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 high-order 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.

    AB - 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 high-order 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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    Cheraghian A, Hajati F, Mian A, Yongsheng G, Gheisari S. 3D face recognition using topographic high-order derivatives. In Proceedings of 20th IEEE International Conference on Image Processing (ICIP), 2013. Australia: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 3705-3709 https://doi.org/10.1109/ICIP.2013.6738764