Unrolled IPPG: Video Heart Rate Esitmation via Unrolling Proximal Gradient Descent


Imaging photoplethysmography (iPPG) is the process of estimating a person’s heart rate from video. In this work, we propose Un- rolled iPPG, in which we integrate iterative optimization updates with deep learning-based signal priors to estimate the pulse wave- form and heart rate from facial videos. We model the signal extracted from video as the sum of an underlying pulse signal and noise, but instead of explicitly imposing a handcrafted prior (e.g., sparsity in the frequency domain) on the signal, we learn priors on the signal and noise using neural networks. We solve for the underlying pulse sig- nal by unrolling proximal gradient descent; the algorithm alternates between gradient descent steps and application of learned denoisers, which replace handcrafted priors and their proximal operators. Using this method, we achieve state-of-the-art heart rate estimation on the challenging MMSE-HR dataset.


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