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

By Anis Kacem, Mohamed Daoudi, Boulbaba Ben Amor, Juan Carlos Álvarez Paiva. 2017

41 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

1. Han-Jia Ye
2. De-chuan Zhan
3. Da-Wei Zhou
4. Yue Gao
5. Junjie Zhu
6. Bingjun Luo
7. Xibin Zhao
8. Sicheng Zhao

Citations in the corpus

• Co-Transport for Class-Incremental Learning (2021, methodology): “Following [17, 51, 69], we select 6 basic expressions (without neutral) as the experimental data.”
• IExpressNet: Facial Expression Recognition with Incremental Classes (2020, methodology): “Following the previous works [27, 71, 75] for FER, we select 6 basic expressions as the experimental data of this paper.”