Synthetic Generation of Face Videos with Plethysmograph Physiology

Zhen Wang1 Yunhao Ba1 Pradyumna Chari1 Oyku Deniz Bozkurt1 Gianna Brown1 Parth Patwa1 Niranjan Vaddi1 Laleh Jalilian1 Achuta Kadambi1

University of California, Los Angeles1

CVPR 2022, New Orleans

image Our proposed framework has successfully incorporated pulsatile signals into the reference image. The estimated pulse waves from PRN exhibit high correlation to the ground-truth waves, and the heart rates are preserved in the frequency domain.

Abstract
Accelerated by telemedicine, advances in Remote Photoplethysmography (rPPG) are beginning to offer a viable path toward non-contact physiological measurement. Unfortunately, the datasets for rPPG are limited as they require videos of the human face paired with ground-truth, synchronized heart rate data from a medical-grade health monitor. Also troubling is that the datasets are not inclusive of diverse populations, i.e., current real rPPG facial video datasets are imbalanced in terms of races or skin tones, leading to accuracy disparities on different demographic groups. This paper proposes a scalable biophysical learning based method to generate physio-realistic synthetic rPPG videos given any reference image and target rPPG signal and shows that it could further improve the state-of-the-art physiological measurement and reduces the bias among different groups. We also collect a largest rPPG dataset of its kind (UCLA-rPPG) with a diverse presence of subject skin tones, in the hope that this could serve as a benchmark dataset for different skin tones in this area and ensure that advances of the technique can benefit all people for healthcare equity.


Files

  • Paper (Link)
  • Dataset (Due to IRB protocol, please fill in Data Request Form for complete dataset.)
    Sample videos available here
  • Code (Forthcoming)


Citations
@inproceedings{wang2022synthetic,
  title={Synthetic Generation of Face Videos with Plethysmograph Physiology},
  author={Wang, Zhen and Ba, Yunhao and Chari, Pradyumna and Bozkurt, Oyku Deniz and Brown, Gianna and Patwa, Parth and Vaddi, Niranjan and Jalilian, Laleh and Kadambi, Achuta},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2022}
}


Contact
Zhen Wang
Electrical and Computer Engineering Department
zhenwang@ucla.edu