Modeling 3D faces from samplings via compressive sensing

Qi Sun, Yanlong Tang, Ping Hu

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    3D data is easier to acquire for family entertainment purpose today because of the mass-production, cheapness and portability of domestic RGBD sensors, e.g., Microsoft Kinect. However, the accuracy of facial modeling is affected by the roughness and instability of the raw input data from such sensors. To overcome this problem, we introduce compressive sensing (CS) method to build a novel 3D super-resolution scheme to reconstruct high-resolution facial models from rough samples captured by Kinect. Unlike the simple frame fusion super-resolution method, this approach aims to acquire compressed samples for storage before a high-resolution image is produced. In this scheme, depth frames are firstly captured and then each of them is measured into compressed samples using sparse coding. Next, the samples are fused to produce an optimal one and finally a high-resolution image is recovered from the fused sample. This framework is able to recover 3D facial model of a given user from compressed simples and this can reducing storage space as well as measurement cost in future devices e.g., single-pixel depth cameras. Hence, this work can potentially be applied into future applications, such as access control system using face recognition, and smart phones with depth cameras, which need high resolution and little measure time.

    Original languageEnglish (US)
    Title of host publicationFifth International Conference on Digital Image Processing, ICDIP 2013
    StatePublished - 2013
    Event5th International Conference on Digital Image Processing, ICDIP 2013 - Beijing, China
    Duration: Apr 21 2013Apr 22 2013

    Publication series

    NameProceedings of SPIE - The International Society for Optical Engineering
    ISSN (Print)0277-786X
    ISSN (Electronic)1996-756X


    Conference5th International Conference on Digital Image Processing, ICDIP 2013


    • 3D face modeling
    • Kinect
    • compressive sensing
    • low-sampling rate requirement
    • super-resolution

    ASJC Scopus subject areas

    • Electronic, Optical and Magnetic Materials
    • Condensed Matter Physics
    • Computer Science Applications
    • Applied Mathematics
    • Electrical and Electronic Engineering


    Dive into the research topics of 'Modeling 3D faces from samplings via compressive sensing'. Together they form a unique fingerprint.

    Cite this