Unrolled iPPG: Video Heart Rate Estimation 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 Unrolled iPPG, in which we integrate iterative optimization updates with deep learning-based signal priors to estimate the pulse waveform 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 signal 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.