The Riemannian and Affine Geometry of Facial Expression and Action Recognition

Mohamed Daoudi (), Juan-Carlos Alvarez Paiva () and Anis Kacem ()
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Mohamed Daoudi: University of Lille, CNRS, UMR 9189, CRIStAL, IMT Lille-Douai
Juan-Carlos Alvarez Paiva: University of Lille, CNRS-UMR-8524
Anis Kacem: University of Lille, CNRS, UMR 9189, CRIStAL, IMT Lille-Douai

Chapter Chapter 23 in Handbook of Variational Methods for Nonlinear Geometric Data, 2020, pp 649-673 from Springer

Abstract: 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.

Date: 2020
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DOI: 10.1007/978-3-030-31351-7_23

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