  Remote Photoplethysmography (rPPG) aims to measure physiological signals and
Heart Rate (HR) from facial videos. Recent unsupervised rPPG estimation methods
have shown promising potential in estimating rPPG signals from facial regions
without relying on ground truth rPPG signals. However, these methods seem
oblivious to interference existing in rPPG signals and still result in
unsatisfactory performance. In this paper, we propose a novel De-interfered and
Descriptive rPPG Estimation Network (DD-rPPGNet) to eliminate the interference
within rPPG features for learning genuine rPPG signals. First, we investigate
the characteristics of local spatial-temporal similarities of interference and
design a novel unsupervised model to estimate the interference. Next, we
propose an unsupervised de-interfered method to learn genuine rPPG signals with
two stages. In the first stage, we estimate the initial rPPG signals by
contrastive learning from both the training data and their augmented
counterparts. In the second stage, we use the estimated interference features
to derive de-interfered rPPG features and encourage the rPPG signals to be
distinct from the interference. In addition, we propose an effective
descriptive rPPG feature learning by developing a strong 3D Learnable
Descriptive Convolution (3DLDC) to capture the subtle chrominance changes for
enhancing rPPG estimation. Extensive experiments conducted on five rPPG
benchmark datasets demonstrate that the proposed DD-rPPGNet outperforms
previous unsupervised rPPG estimation methods and achieves competitive
performances with state-of-the-art supervised rPPG methods.
