Welcome to use the code from our paper "Biometric Authentication based on Enhanced Remote Photoplethysmography Signal Morphology". The training weights, data, and results can be downloaded here. Please unzip rppg_biometrics_artifacts.zip to the main folder.
Please see requirement.txt for the required python environment.
The data structures are as follows.
data_example
├── h5_obf # this folder contain data examples from OBF dataset
├── 001_xx_1.h5 # 001 means the subject ID number. 1 means the first session (pre-exercise)
├── imgs # shape (T, 6, 6, 3), the downsampled videos frames,permutation will be performaned during loading data. T is the number of frames, 6 is the spatial dimension, 3 is the RGB channels.
├── bvp # shape (T,), the ground truth cPPG signal. NOT USED DURING TRAINING. only used for morphology evaluation.
├── 001_xx_2.h5 # 001 means the subject ID number. 2 means the second session (post-exercise)
.
├── 100_xx_1.h5
├── 100_xx_2.h5
├── external_cppg.h5 # A combination of cPPG biometrics datasets including Biosec2, BIDMC, and PRRB for rPPG-cPPG hybrid training.
if ./data_example/h5_obf contains the complete OBF data, one can run python rppg_model_pretraining.py to start the 1st training stage. More details can be found in the comments of the .py file.
Weights and irrelevant power ratios (IPR) are saved during training at ./results (The training records are already in the folder.). The best weight is chosen at the lowest IPR by ./notebooks/sacred_train.ipynb. The chosen weight are stored at ./rppg_model_pretrained_weights.pt, which will be used in the 2nd training stage.
if ./data_example/h5_obf contains the complete OBF data and ./data_example/external_cppg.h5 exists, one can run python joint_rppg_cppg_hybrid_training.py to start the 2nd training stage. More details can be found in the comments of the .py file. Weights are saved during training. joint_inference.py is used to get the ID prediction results and the rPPG signals at different epochs as shown below.
for epoch in {0..29}
do
python joint_inference.py with train_exp_num=1 e=$(awk "BEGIN {print(${epoch}*100)}") -i $(awk "BEGIN {print(${epoch}*100)}")
done
The training records after the 2nd training stage are in ./joint_results/default/1. One can run python eval_eer_auc.py to get the EER and AUC metrics. One can run python eval_morph.py to get the Pearson correlation results for the morphology evaluation. More details can be found in the comments of the .py files.
@article{sun2024biometrics,
title={Biometric Authentication Based on Enhanced Remote Photoplethysmography Signal Morphology},
author={Sun, Zhaodong and Li, Xiaobai and Komulainen, Jukka and Zhao, Guoying},
booktitle={International Joint Conference on Biometrics (IJCB)},
year={2024},
}