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 paperpeer-review

    229 Downloads (Pure)


    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
    ISBN (Print)9781479923410
    Publication statusPublished - 2013
    Event2013 IEEE International Conference on Image Processing - Melbourne, Australia, Melbourne, Australia
    Duration: 15 Sept 201318 Sept 2013
    Conference number: 20th


    Conference2013 IEEE International Conference on Image Processing
    Abbreviated titleICIP 2013


    Dive into the research topics of '3D face recognition using topographic high-order derivatives'. Together they form a unique fingerprint.

    Cite this