RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation
A reproduction of the RhythmNet model. Paper link
Dataset:
VIPL-HR dataset
Experiments
Shared parameters:
batch size: 32
Dataset: VIPL
Model: RhythmNet
initial learning rate: 1e-3
epochs: 50
window_size = 300 frames with stride of 0.5 seconds
Dataset-split: 5 fold validation
Experiment for 1-Fold without GRU layer
| Set | Loss | MAE (bpm) | RMSE (bpm) |
|---|---|---|---|
| Training | 3.096 | 1.817 | 2.834 |
| Eval | 15.91 | 9.255 | 11.787 |
Experiment for 1-Fold with GRU layer
| Set | Loss | MAE (bpm) | RMSE (bpm) |
|---|---|---|---|
| Training | 3.925 | 2.423 | 4.16 |
| Eval | 14.25 | 13.992 | 17.019 |