研究背景：遠程光體積描記法（remote photoplethysmography, rPPG）用於心率估計是一個具潛力且快速發展的研究領域，現今最先進的方法多採用擴散模型、時空卷積神經網路（spatiotemporal CNN）以及基於 Transformer 的架構，以降低預測誤差。然而，該領域仍存在三項主要不足：(1) 非端到端系統過度依賴手工特徵，導致在不同場景下表現不穩定；(2) 二維人臉對齊方法在頭部運動時準確率下降；(3) 現有資料集過於簡單，無法反映真實世界。
研究目的：本研究目標有二：(1) 比較非端到端與端到端深度學習模型在遠程心率估計中的效能差異；(2) 評估二維人臉對齊方法（MTCNN）與三維人臉對齊方法（PRNet）在提升遠程心率估計精度的效果。本研究旨在釐清人臉對齊方式與模型架構對遠程心率量測精度的影響。
研究方法：我們使用兩個資料集：一個為專為 rPPG 研究設計的自建資料集，另一個為公開的 UBFC-RPPG 資料集。非端到端模型部分，我們使用十種傳統 rPPG 訊號擷取方法並搭配 LSTM 網路；端到端模型部分則採用 PhysFormer++，直接將原始臉部影片轉換為遠程心率預測。在人臉對齊方面，我們分別使用二維的 MTCNN 與三維的 PRNet。效能評估採用心率估計的均方根誤差，並涵蓋不同光照、運動及頭部運動等實驗條件。
研究結果：結果顯示，端到端深度學習模型效能優於非端到端方法，在 UBFC 資料集上可降低約 0.33 bpm 的 RMSE。在較複雜的資料集中，LSTM 模型無法有效捕捉有意義的訊號模式，而 PhysFormer++ 仍具穩健表現。此外，當 PhysFormer++ 搭配三維人臉對齊（PRNet）時，其在動態頭部運動場景中的表現顯著優於二維對齊（MTCNN）。例如，在「旋轉」測試中，PRNet 相較 MTCNN 將心率估計 RMSE 降低 10.91 bpm；在騎車實驗中，PRNet 分別在等級 1、3、5 將 RMSE 降低 9.13 bpm、12.48 bpm 與 8.47 bpm。

Introduction: Remote photoplethysmography (rPPG) for heart rate estimation is a promising and rapidly evolving field, with over 200 peer-reviewed papers now addressing the topic. State-of-the-art methods employ diffusion models, spatiotemporal CNNs, and transformer-based architectures to reduce prediction errors. Despite these advancements, three shortcomings remain: (1) Non-end-to-end systems rely heavily on handcrafted features, making them unreliable;(2) 2D face alignment methods degrade in accuracy under head movement; (3) Existing datasets are too simple and do not reflect real-world conditions.
Objectives: The objective is twofold: (1) to compare the performance of non-end-to-end and end-to-end deep learning models in the context of remote HR estimation, and (2) to evaluate the effectiveness of 2D face alignment (MTCNN) versus 3D face alignment (PRNet) in improving remote HR accuracy. This comparison will provide insights into how face alignment methods and model architectures affect the precision of remote heart rate measurement.
Methods: We utilized two distinct datasets: a custom dataset specifically designed for rPPG research and the publicly available UBFC-RPPG dataset. Ten non-end-to-end models relying on different traditional rppg-extraction methods followed by LSTM networks, were compared to the end-to-end PhysFormer++ model, which directly maps raw facial video data to remote heart rate predictions. For face alignment, we employed both 2D MTCNN and 3D PRNet. Performance was evaluated using Root Mean Square Error (RMSE) for remote heart rate estimation across various experimental conditions, including changes in lighting, motion artifacts, and head movements.
Results: The results demonstrate that end-to-end deep learning models outperform non-end-to-end approaches, achieving an RMSE reduction of approximately 0.33 bpm on the UBFC dataset. On our more complex dataset, the LSTM model failed to capture meaningful patterns, whereas PhysFormer++ delivered robust performance. Furthermore, when combined with 3D face alignment within the PhysFormer++ pipeline, PRNet markedly surpasses 2D alignment via MTCNN under dynamic head movements. For instance, in the ``rotate'' test, PRNet reduced heart rate estimation RMSE by 10.91 bpm compared to MTCNN, and in the bike experiments at levels 1, 3, and 5, PRNet achieved RMSE reductions of 9.13 bpm, 12.48 bpm, and 8.47 bpm, respectively.

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