Remote heart rate estimation is the measurement of heart rate without any
physical contact with the subject and is accomplished using remote
photoplethysmography (rPPG) in this work. rPPG signals are usually collected
using a video camera with a limitation of being sensitive to multiple
contributing factors, e.g. variation in skin tone, lighting condition and
facial structure. End-to-end supervised learning approach performs well when
training data is abundant, covering a distribution that doesn't deviate too
much from the distribution of testing data or during deployment. To cope with
the unforeseeable distributional changes during deployment, we propose a
transductive meta-learner that takes unlabeled samples during testing
(deployment) for a self-supervised weight adjustment (also known as
transductive inference), providing fast adaptation to the distributional
changes. Using this approach, we achieve state-of-the-art performance on
MAHNOB-HCI and UBFC-rPPG.

本文提出了一种基于远程光电容积脉搏图（rPPG）的无接触式心率估计方法，通过自监督权重调整来实现在测试部署期间对未标记样本的快速适应以应对未知分布变化的能力，从而取得了 MAHNOB-HCI 和 UBFC-rPPG 方面的最优性能。

使用迁移元学习器进行远程心率估计的元 rPPG

Meta-rPPG: Remote Heart Rate Estimation Using a Transductive  Meta-Learner

This paper presents a novel method for remote heart rate (HR) estimation.
Recent studies have proved that blood pumping by the heart is highly correlated
to the intense color of face pixels, and surprisingly can be utilized for
remote HR estimation. Researchers successfully proposed several methods for
this task, but making it work in realistic situations is still a challenging
problem in computer vision community. Furthermore, learning to solve such a
complex task on a dataset with very limited annotated samples is not
reasonable. Consequently, researchers do not prefer to use the deep learning
approaches for this problem. In this paper, we propose a simple yet efficient
approach to benefit the advantages of the Deep Neural Network (DNN) by
simplifying HR estimation from a complex task to learning from very correlated
representation to HR. Inspired by previous work, we learn a component called
Front-End (FE) to provide a discriminative representation of face videos,
afterward a light deep regression auto-encoder as Back-End (BE) is learned to
map the FE representation to HR. Regression task on the informative
representation is simple and could be learned efficiently on limited training
samples. Beside of this, to be more accurate and work well on low-quality
videos, two deep encoder-decoder networks are trained to refine the output of
FE. We also introduce a challenging dataset (HR-D) to show that our method can
efficiently work in realistic conditions. Experimental results on HR-D and
MAHNOB datasets confirm that our method could run as a real-time method and
estimate the average HR better than state-of-the-art ones.

本文提出了一种新颖的方法来进行远程心率估计，并通过前端和后端网络实现了实时、高效的心率测量，可应用于低质量视频的情境中。