Periodic Variance Maximization using Generalized Eigenvalue Decomposition applied to Remote Photoplethysmography estimation

Abstract : A generic periodic variance maximization algorithm to extract periodic or quasi-periodic signals of unknown periods embedded into multi-channel temporal signal recordings is described in this paper. The algorithm combines the notion of maximizing a periodicity metric combined with the global optimization scheme to estimate the source periodic signal of an unknown period. The periodicity maximization is performed using Generalized Eigenvalue Decomposition (GEVD) and the global optimization is performed using tabu search. A case study of remote photoplethysmography signal estimation has been utilized to assess the performance of the method using videos from public databases UBFC-RPPG [1] and MMSE-HR [31]. The results confirm the improved performance over existing state of the art methods and the feasibility of the use of the method in a live scenario owing to its small execution time.
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Communication dans un congrès
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Jun 2018, Salt Lake City, UT, United States. 〈http://cvpr2018.thecvf.com/〉
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01830541
Contributeur : Yannick Benezeth <>
Soumis le : jeudi 5 juillet 2018 - 10:36:59
Dernière modification le : mardi 17 juillet 2018 - 01:13:15

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Richard Macwan, Serge Bobbia, Yannick Benezeth, Julien Dubois, Alamin Mansouri. Periodic Variance Maximization using Generalized Eigenvalue Decomposition applied to Remote Photoplethysmography estimation. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Jun 2018, Salt Lake City, UT, United States. 〈http://cvpr2018.thecvf.com/〉. 〈hal-01830541〉

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