Remote PPG Estimation from RGB-NIR Facial Image Sequence for Heart Rate Estimation

This paper presents a dual-modal (RGB-NIR) technique to estimate remote photoplethysmogram (rPPG) signal, i.e. the heart rate, from a facial image sequence. We developed denoising techniques with a modified amplitude selective filtering (ASF), wavelet decomposition and robust principal component analysis (RPCA), to enhance the uncovering of the rPPG signal through the well-known ICA algorithm. A new dataset built with RealSense RGB-D camera is considered in experiments: regular brightness, under-illumination, and face motion. Experimental results show that the proposed method has reached competitive performance among the state-of-the-art methods in motion and under-illuminated scenarios even at a shorter input video length (10 to 20 seconds).