  Remote photoplethysmography (rPPG) technique extracts blood volume pulse
(BVP) signals from subtle pixel changes in video frames. This study introduces
rFaceNet, an advanced rPPG method that enhances the extraction of facial BVP
signals with a focus on facial contours. rFaceNet integrates identity-specific
facial contour information and eliminates redundant data. It efficiently
extracts facial contours from temporally normalized frame inputs through a
Temporal Compressor Unit (TCU) and steers the model focus to relevant facial
regions by using the Cross-Task Feature Combiner (CTFC). Through elaborate
training, the quality and interpretability of facial physiological signals
extracted by rFaceNet are greatly improved compared to previous methods.
Moreover, our novel approach demonstrates superior performance than SOTA
methods in various heart rate estimation benchmarks.
