HemNet: Hemoglobin-Assistant Network for Video-Based Remote Photoplethysmography Measurement

Traditional skin-contact physical sensors typically detect changes of blood volume to predict the periodicity of heartbeat by analyzing the absorption spectra of hemoglobin. However, the contact on human skin may cause uncomfortable feeling and induce difficulty for long-term monitoring. Recently, video-based remote photoplethysmography (rPPG) estimation approaches analyze the periodic facial color changes for matching cardiac cycle in a contactless manner. Nevertheless, the inherent relationship between the changes of facial color and blood volume is not fully exploited. Besides the influence of blood volume (i.e., hemoglobin), there are also other factors such as lighting and reflection that cause the change on facial color. We exploit the physical principles that cause skin color variations to separate the hemoglobin factor driven by blood volume. Based on the physical prior of the reflection of human skin, we introduce an rPPG estimation network assisted by decoupled hemoglobin sequence, named HemNet, which first explicitly leverages hemoglobin to assist rPPG signal estimation. To obtain meaningful hemoglobin from facial video, we design a human skin color disentangler that decouples the facial color variations into four significant features, i.e., hemoglobin, melanin, shading, and specular. We then present a multi-modality rPPG estimator that utilizes cross-covariance attention to extract fused feature from hemoglobin and RGB video inputs. Finally, an adaptive negative Pearson loss is proposed to effectively address phase misalignment between the blood volume in the finger and facial region during the training phase. We evaluate our HemNet on four widely used public benchmark datasets. The superiority of our method is demonstrated in both intra-dataset and cross-dataset test settings. The code is available at https://github.com/jingang-cv/hemnet