 |
 |
Communication dans un congrès
Parameter-free adaptive step-size multiobjective optimization applied to remote photoplethysmography
Abstract : In this work, we propose to reformulate the objective function of Independent Component Analysis (ICA) to make it a better posed problem in the context of Remote photoplethysmography (rPPG). In recent previous works, linear combination coefficients of RGB channels are estimated maximizing the non-Gaussianity of ICA output components. However, in the context of rPPG a priori knowledge of the pulse signal can be incorporated into the component extraction algorithm. To this end, the contrast function of regular ICA is extended with a measure of periodicity formulated using autocorrelation. This novel semi-blind source extraction method for measuring rPPG has the interesting property of being free from manual parameter adjustment. The tedious selection of the step-size parameter in the gradient-ascent algorithm has been advantageously replaced by an adaptive step size. Our method has been validated against our large in-house video database UBFC-RPPG.
Littérature citée [21 références]
https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01678241
Contributeur : Yannick Benezeth <>
Soumis le : mardi 9 janvier 2018 - 10:08:20
Dernière modification le : lundi 30 mars 2020 - 08:53:02
Contributeur : Yannick Benezeth <>
Soumis le : mardi 9 janvier 2018 - 10:08:20
Dernière modification le : lundi 30 mars 2020 - 08:53:02
Fichier
BHI_MAICA_2018.pdf
Fichiers produits par l'(les) auteur(s)
