Paper Info
Title
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.
Year
DOI
Venue
2018
10.1109/CVPRW.2018.00181
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Keywords
Field
DocType
generic periodic variance maximization algorithm,quasiperiodic signals,multichannel temporal signal recordings,periodicity metric,global optimization scheme,source periodic signal,periodicity maximization,remote photoplethysmography signal estimation,Generalized Eigenvalue Decomposition,UBFC-RPPG public database,MMSE-HR public database
Periodic function,Pattern recognition,Global optimization,Photoplethysmogram,Computer science,Algorithm,Generalized eigenvalue decomposition,Artificial intelligence,Execution time,Periodic graph (geometry),Maximization,Tabu search
Conference
ISSN
ISBN
Citations 
2160-7508
978-1-5386-6101-7
0
PageRank 
References 
Authors
0.34
16
5
Name
Order
Citations
PageRank
Richard Macwan1165.11
Serge Bobbia200.34
Yannick Benezeth339926.11
Julien Dubois414618.76
Alamin Mansouri513722.29