PhysFlow: Skin tone transfer for remote heart rate estimation through conditional normalizing flows


Joaquim Comas Martinez (Universitat Pompeu Fabra), Antonia Alomar (Universitat Pompeu Fabra), Adria Ruiz (CSIC-UPC), Federico Sukno (Pompeu Fabra University)
The 35th British Machine Vision Conference
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Abstract

In recent years, deep learning methods have shown impressive results for camera-based remote physiological signal estimation, clearly surpassing traditional methods. However, the performance and generalization ability of Deep Neural Networks heavily depends on rich training data truly representing different factors of variation encountered in real applications. Unfortunately, many current remote photoplethysmography (rPPG) datasets lack diversity, particularly in darker skin tones, leading to biased performance of existing rPPG approaches. To mitigate this bias, we introduce PhysFlow, a novel method for augmenting skin diversity in remote heart rate estimation using conditional normalizing flows. PhysFlow adopts end-to-end training optimization, enabling simultaneous training of supervised rPPG approaches on both original and generated data. Additionally, we condition our model using CIELAB color space skin features directly extracted from the facial videos without the need for skin-tone labels. We validate PhysFlow on publicly available datasets, UCLA-rPPG and MMPD, demonstrating reduced heart rate error, particularly in dark skin tones. Furthermore, we demonstrate its versatility and adaptability across different data-driven rPPG methods.

Citation

@inproceedings{Martinez_2024_BMVC,
author    = {Joaquim Comas Martinez and Antonia Alomar and Adria Ruiz and Federico Sukno},
title     = {PhysFlow: Skin tone transfer for remote heart rate estimation through conditional normalizing flows},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0136.pdf}
}


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