摘要
Remote photoplethysmography (rPPG) is a non-contact method for heart rate (HR) estimation from facial videos. In this paper, we propose a novel two-stream convolutional neural network for remote HR estimation. We introduce a feature extraction stream by adopting a low-rank constraint to guide the network to learn a robust feature representation. We also develop a complementary stream, the rPPG extraction stream, to extract reliable rPPG signals from facial regions. After fusing the two streams, we develop a unified neural network to learn the feature extraction and to estimate HR simultaneously. Experimental results on COHFACE dataset demonstrate that our proposed method achieves state-of-the-art performance for HR estimation.