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The Riemannian and Affine Geometry of Facial Expression and Action Recognition

In: Handbook of Variational Methods for Nonlinear Geometric Data

Author

Listed:
  • Mohamed Daoudi

    (University of Lille, CNRS, UMR 9189, CRIStAL, IMT Lille-Douai)

  • Juan-Carlos Alvarez Paiva

    (University of Lille, CNRS-UMR-8524
    Painlevé Laboratory)

  • Anis Kacem

    (University of Lille, CNRS, UMR 9189, CRIStAL, IMT Lille-Douai)

Abstract

Recent advances in human 2D and 3D landmarks tracking have made it possible to model facial expression and action recognition as a temporal sequence of landmarks. We work directly with the Euclidean or affine invariants of landmarks. These invariants are represented as points in different shape spaces (Positive Semi-Definite (PSD) manifold, Grassmann manifold) and therefore their temporal evolution can be seen as a trajectory in these spaces. Using Riemannian geometry, these trajectories can be compared and classified, which has immediate applications in facial expression and action recognition.

Suggested Citation

  • Mohamed Daoudi & Juan-Carlos Alvarez Paiva & Anis Kacem, 2020. "The Riemannian and Affine Geometry of Facial Expression and Action Recognition," Springer Books, in: Philipp Grohs & Martin Holler & Andreas Weinmann (ed.), Handbook of Variational Methods for Nonlinear Geometric Data, chapter 0, pages 649-673, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-31351-7_23
    DOI: 10.1007/978-3-030-31351-7_23
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