rPPG Estimation: Vision Transformer With 3-D Temporal Central Difference
Bibliographic record
Abstract
Remote photoplethysmography (rPPG) has gained increasing importance, especially during and after the COVID-19 pandemic, for its ability to estimate heart rate (HR) by analyzing subtle variations in skin color without physical contact. This noninvasive and practical method relies on capturing changes in pixel intensity through RGB video or near-infrared imaging. In this study, we propose a novel hybrid model that leverages the feed-forward integration of 3-D convolutional neural networks (3-D-CNNs) and video vision transformers (ViViTs) to enhance rPPG estimation. The 3-D-CNNs first capture local spatiotemporal features, using temporal central difference convolution (3DCDC-T) and convolutional block attention module (CBAM). These local features are then passed to the ViViT, where multihead self-attention (MHSA) captures global contextual relationships and long-range dependencies across frames, enabling a more effective representation of complex temporal dynamics. This sequential learning allows the model to progressively refine features from local to global, ensuring more consistent and coherent feature extraction. Our feed-forward approach also improves computational efficiency by reducing the dimensionality of the input data before global attention processing, making it particularly effective in data-limited environments. Through comprehensive experiments, we show that our hybrid approach outperforms state-of-the-art methods across multiple public datasets, achieving a 22.55% improvement in mean absolute error (MAE) and a 55.80% improvement in root mean-squared error (RMSE) on the UBFC-rPPG dataset, demonstrating superior feature progression and generalization in rPPG and HR estimation tasks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".