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Related Experiment Video

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Multilinear Modelling of Faces and Expressions.

Stella Grasshof, Hanno Ackermann, Sami Sebastian Brandt

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 20, 2020
    PubMed
    Summary

    This study introduces a novel 3D statistical face model using tensor factorization. It reveals that facial expressions form a star-shaped structure intersecting at apathy, not neutral expressions.

    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Machine Learning

    Background:

    • Statistical 3D face models are crucial for facial analysis and synthesis.
    • Existing models often assume expressions originate from a neutral state, limiting realism.

    Purpose of the Study:

    • To develop a versatile 3D multilinear statistical face model.
    • To investigate the underlying structure of facial expression subspaces.
    • To propose a novel 3D face reconstruction method from 2D images.

    Main Methods:

    • Tensor factorization of 3D face scans to decompose shape into person and expression subspaces.
    • Analysis of expression subspace revealing a low-dimensional, star-shaped structure.
    • Reparameterization of the expression subspace using a fourth-order moment tensor centered at apathy.

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  • 3D face reconstruction from 2D projections using an uncalibrated projective camera model.

    • Main Results:

    • Identified a novel star-shaped structure in the facial expression subspace.
    • Discovered that expression trajectories intersect at an 'apathetic' expression, not neutral.
    • Successfully separated person and expression subspaces in 3D face reconstruction.
    • Demonstrated flexible and natural expression modeling for diverse faces.

    Conclusions:

    • The proposed tensor-based face model offers a more accurate representation of facial expressions.
    • The findings on expression subspace structure enable more realistic facial synthesis and manipulation.
    • The reconstruction method effectively handles non-linearity from perspective projection.