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In this paper, we study the problem of facial expression recognition using a novel space-time geometric representation. We describe the temporal evolution of facial landmarks as parametrized trajectories on the Riemannian manifold of positive semidefinite matrices of fixed-rank. Our representation has the advantage to bring naturally a second desirable quantity when comparing shapes – the spatial...
We study the problem of classifying actions of human subjects using depth movies generated by Kinect or other depth sensors. Representing human body as dynamical skeletons, we study the evolution of their (skeletons’) shapes as trajectories on Kendall’s shape manifold. The action data is typically corrupted by large variability in execution rates within and across subjects and, thus, causing major...
This paper proposes a new framework for online detection of spontaneous emotions from low-resolution depth sequences of the upper part of the body. To face the challenges of this scenario, depth videos are decomposed into subsequences, each modeled as a linear subspace, which in turn is represented as a point on a Grassmann manifold. Modeling the temporal evolution of distances between subsequences...
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