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.
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Communication dans un congrès
IEEE International Conference on Biomedical and Health Informatics, 2018, Las Vegas, United States
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Contributeur : Yannick Benezeth <>
Soumis le : mardi 9 janvier 2018 - 10:08:20
Dernière modification le : samedi 14 juillet 2018 - 01:05:34

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Richard Macwan, Yannick Benezeth, Keisuke Nakamura, Randy Gomez, Yadong Wu, et al.. Parameter-free adaptive step-size multiobjective optimization applied to remote photoplethysmography. IEEE International Conference on Biomedical and Health Informatics, 2018, Las Vegas, United States. 〈hal-01678241〉

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