NAS-HR: Neural architecture search for heart rate estimation from face videos

Abstract

BackgroundIn anticipation of its great potential application to natural human-computer interaction and health monitoring, heart-rate (HR) estimation based on remote photoplethysmography has recently attracted increasing research attention. Whereas the recent deep-learning-based HR estimation methods have achieved promising performance, their computational costs remain high, particularly in mobile-computing scenarios. MethodsWe propose a neural architecture search approach for HR estimation to automatically search a lightweight network that can achieve even higher accuracy than a complex network while reducing the computational cost. First, we define the regions of interests based on face landmarks and then extract the raw temporal pulse signals from the R, G, and B channels in each ROI. Then, pulse-related signals are extracted using a plane-orthogonal-to-skin algorithm, which are combined with the R and G channel signals to create a spatial-temporal map. Finally, a differentiable architecture search approach is used for the network-structure search. ResultsCompared with the state-of-the-art methods on the public-domain VIPL-HR and PURE databases, our method achieves better HR estimation performance in terms of several evaluation metrics while requiring a much lower computational cost1.