6533b835fe1ef96bd12a0141
RESEARCH PRODUCT
Periodic Variance Maximization using Generalized Eigenvalue Decomposition applied to Remote Photoplethysmography estimation
Julien DuboisSerge BobbiaYannick BenezethRichard MacwanAlamin Mansourisubject
Computer sciencebusiness.industry[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing0206 medical engineeringFeature extraction[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]02 engineering and technologyMaximizationVariance (accounting)020601 biomedical engineeringSignalTabu searchPeriodic function[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingMetric (mathematics)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligencebusinessGlobal optimizationAlgorithmdescription
International audience; 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 | journal | country | edition | language |
|---|---|---|---|---|
| 2018-06-18 |