A Novel Space-Time Representation on the Positive Semidefinite Cone for Facial Expression Recognition

Anis Kacem, Mohamed Daoudi, Boulbaba Ben Amor, Juan Carlos Alvarez-Paiva; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 3180-3189

Abstract


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 covariance -- in addition to the conventional affine-shape representation. We derive then geometric and computational tools for rate-invariant analysis and adaptive re-sampling of trajectories, grounding on the Riemannian geometry of the manifold. Specifically, our approach involves three steps: 1) facial landmarks are first mapped into the Riemannian manifold of positive semidefinite matrices of rank 2, to build time-parameterized trajectories; 2) a temporal alignment is performed on the trajectories, providing a geometry-aware (dis-)similarity measure between them; 3) finally, pairwise proximity function SVM (ppfSVM) is used to classify them, incorporating the latter (dis-)similarity measure into the kernel function. We show the effectiveness of the proposed approach on four publicly available benchmarks (CK+, MMI, Oulu-CASIA, and AFEW). The results of the proposed approach are comparable to or better than the state-of-the-art methods when involving only facial landmarks.

Related Material


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[bibtex]
@InProceedings{Kacem_2017_ICCV,
author = {Kacem, Anis and Daoudi, Mohamed and Ben Amor, Boulbaba and Carlos Alvarez-Paiva, Juan},
title = {A Novel Space-Time Representation on the Positive Semidefinite Cone for Facial Expression Recognition},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}