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Generalized Eigenvalue Decomposition Applied to Estimation of Spatial rPPG Distribution of Skin

Abstract : Remote photoplethysmography (rPPG) has been at the forefront recently, thanks to its capacity in estimating non-contact physiological parameters such as heart rate and heart rate variability (Wang et al. in FBB 6:33, 2018). rPPG signals are typically extracted from facial videos by performing spatial averaging to obtain temporal RGB traces. Although this spatial averaging simplifies computation, it is accompanied by loss of essential spatial information which might reveal interesting relationships between signals from different spatial regions. In this article, we present a novel algorithm adapted from generalized eigenvalue decomposition (GEVD) to estimate this spatial rPPG distribution. GEVD is an extremely versatile algorithm that finds uses in signal and image processing and analytical problems such as principal component analysis and Fisher discriminant analysis (
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-03239495
Contributeur : Yannick Benezeth <>
Soumis le : jeudi 27 mai 2021 - 15:01:15
Dernière modification le : vendredi 28 mai 2021 - 03:20:56

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Richard Macwan, Yannick Benezeth, Alamin Mansouri. Generalized Eigenvalue Decomposition Applied to Estimation of Spatial rPPG Distribution of Skin. Journal of Mathematical Imaging and Vision, Springer Verlag, 2021, ⟨10.1007/s10851-021-01025-3⟩. ⟨hal-03239495⟩

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