4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications

Shiyang Cheng, Irene Kotsia, Maja Pantic, Stefanos Zafeiriou

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    The progress we are currently witnessing in many computer vision applications, including automatic face analysis, would not be made possible without tremendous efforts in collecting and annotating large scale visual databases. To this end, we propose 4DFAB, a new large scale database of dynamic high-resolution 3D faces (over 1,800,000 3D meshes). 4DFAB contains recordings of 180 subjects captured in four different sessions spanning over a five-year period. It contains 4D videos of subjects displaying both spontaneous and posed facial behaviours. The database can be used for both face and facial expression recognition, as well as behavioural biometrics. It can also be used to learn very powerful blendshapes for parametrising facial behaviour. In this paper, we conduct several experiments and demonstrate the usefulness of the database for various applications. The database will be made publicly available for research purposes.
    Original languageEnglish
    Title of host publication30th IEEE Conference on Computer Vision and Pattern Recognition : CVPR 2017
    Subtitle of host publication21-26 July 2016, Honolulu, Hawaii : proceedings
    EditorsRama Chellappa, Zhengyou Zhang, Anthony Hoogs
    Number of pages12
    ISBN (Electronic)978-1-5386-0457-1
    ISBN (Print)978-1-5386-0458-8
    Publication statusPublished - 5 Dec 2017
    Event30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Hawaii Convention Center, Honolulu, United States
    Duration: 21 Jul 201726 Jul 2017
    Conference number: 30


    Conference30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
    Abbreviated titleCVPR
    Country/TerritoryUnited States
    Internet address

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