Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner

This repository is the official implementation of Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner that has been accepted to ECCV 2020.

Heatmap Visualization

Left to right:

1. Cropped input image
2. End-to-end trained model (baseline)
3. Meta-rPPG (transducive inference)
4. Top to down: rPPG signal, Power Spectral Density (PSD), Predicted and ground truth heart rate

Requirements

To install requirements:

pip install -r requirements.txt

All experiments can be run on a single NVIDIA GTX1080Ti GPU.

The code was tested with python3.6 the following software versions:

Training

Training Data Preparation

Download training data (example.pth) from Google Drive. Due to privacy issue (face images), provided data contains only a subset of the entire training data, i.e. contains faces of the authors of this paper.

Move example.pth to data/ directory:

mv example.pth data/

Begin Training

To begin training, run:

python3 train.py

Validation Data

Validation data can be requested from:

MAHNOB-HCI

UBFC-rPPG

Contributing

If you find this work useful, consider citing our work using the following bibTex:

@inproceedings{lee2020meta,
  title={Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner},
  author={Lee, Eugene and Chen, Evan and Lee, Chen-Yi},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2020}
}