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.
University of Oulu（奥卢大学）；Xi’an Jiaotong University（西安交通大学）；Northwest University（西北大学）
Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications (e.g., remote healthcare). Existing rPPG approaches rely on analyzing very fine details of facial videos, which are prone to be affected by video compression. Here we propose a two-stage, end-to-end method using hidden rPPG information enhancement and attention networks, which is the first attempt to counter video compression loss and recover rPPG signals from highly compressed videos. The method includes two parts: 1) a Spatio-Temporal Video Enhancement Ne…
Vicarious Perception Technologies (VicarVision)（代理感知技术公司（VicarVision））；Delft University of Technology（代尔夫特理工大学）
Remote photo-plethysmography (rPPG) uses a camera to estimate a person's heart rate (HR). Similar to how heart rate can provide useful information about a person's vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine fluctuations in the intervals between heart beats. However, this measure requires temporally locating heart beats with a high degree of precision. We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not only estimates heart…
Reliability-Aware Weighted Multi-Scale Spatio-Temporal Maps for Heart Rate Monitoring
CVPR Unit, Indian Statistical Institute（印度统计研究所计算机视觉与模式识别单元）；Department of Computational and Data Sciences, Indian Institute of Science（印度科学研究所计算与数据科学系）
Remote photoplethysmography (rPPG) monitors heart rate without requiring physical contact, which allows for a wide variety of applications. Deep learning-based rPPG have demonstrated superior performance over the traditional approaches in controlled context. However, the lighting situation in indoor space is typically complex, with uneven light distribution and frequent variations in illumination. It lacks a fair comparison of different methods under different illuminations using the same dataset. In this paper, we present a public dataset, namely the BH-rPPG dataset, which contains data from …
Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation
Sber AI Lab（Sber AI实验室）；Samara State Medical University（萨马拉州医学大学）；ISP RAS Research Center for Trusted Artificial Intelligence（俄罗斯科学院信息与通信技术研究所可信人工智能研究中心）
Vicarious Perception Technologies（Vicarious Perception Technologies公司）；Eindhoven University of Technology（埃因霍温理工大学）；Delft University of Technology（代尔夫特理工大学）
Remote photo-plethysmography (rPPG) uses a remotely placed camera to estimating a person's heart rate (HR). Similar to how heart rate can provide useful information about a person's vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine fluctuations in the intervals between heart beats. However, this measure requires temporally locating heart beats with a high degree of precision. We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not on…
Camera-based physiological monitoring, especially remote photoplethysmography (rPPG), is a promising tool for health diagnostics, and state-of-the-art pulse estimators have shown impressive performance on benchmark datasets. We argue that evaluations of modern solutions may be incomplete, as we uncover failure cases for videos without a live person, or in the presence of severe noise. We demonstrate that spatiotemporal deep learning models trained only with live samples "hallucinate" a genuine-shaped pulse on anomalous and noisy videos, which may have negative consequences when rPPG models are…
Recent studies demonstrated that the average heart rate (HR) can be measured from facial videos based on non-contact remote photoplethysmography (rPPG). However for many medical applications (e.g., atrial fibrillation (AF) detection) knowing only the average HR is not sufficient, and measuring precise rPPG signals from face for heart rate variability (HRV) analysis is needed. Here we propose an rPPG measurement method, which is the first work to use deep spatio-temporal networks for reconstructing precise rPPG signals from raw facial videos. With the constraint of trend-consistency with ground…
Center for Machine Vision and Signal Analysis, University of Oulu（奥卢大学机器视觉与信号分析中心）；Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所智能信息处理重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications (e.g., remote healthcare). Existing end-to-end rPPG and heart rate (HR) measurement methods from facial videos are vulnerable to the less-constrained scenarios (e.g., with head movement and bad illumination). In this letter, we explore the reason why existing end-to-end networks perform poorly in challenging conditions and establish a strong end-to-end baseline (AutoHR) for remote HR measurement with neural architecture search (NAS). The proposed method inc…
This paper studies the problem of reproducible research in remote photoplethysmography (rPPG). Most of the work published in this domain is assessed on privately-owned databases, making it difficult to evaluate proposed algorithms in a standard and principled manner. As a consequence, we present a new, publicly available database containing a relatively large number of subjects recorded under two different lighting conditions. Also, three state-of-the-art rPPG algorithms from the literature were selected, implemented and released as open source free software. After a thorough, unbiased experim…
This work introduces a novel DeepFake detection framework based on physiological measurement. In particular, we consider information related to the heart rate using remote photoplethysmography (rPPG). rPPG methods analyze video sequences looking for subtle color changes in the human skin, revealing the presence of human blood under the tissues. In this work we investigate to what extent rPPG is useful for the detection of DeepFake videos. The proposed fake detector named DeepFakesON-Phys uses a Convolutional Attention Network (CAN), which extracts spatial and temporal information from video fr…
Heart rate measuring based on remote photoplethysmography (rPPG) plays an important role in health caring, which estimates heart rate from facial video in a non-contact, less-constrained way. End-to-end neural network is a main branch of rPPG-based heart rate estimation methods, whose trait is recovering rPPG signal containing sufficient heart rate message from original facial video directly. However, there exists some easily neglected problems on relevant datasets which thwarting the efficient training of end-to-end methods, such as uncertain temporal delay and indefinite envelope shape of la…
Beihang University（北京航空航天大学）；Zhejiang University of Technology（浙江工业大学）
With the improvement of sensor technology and significant algorithmic advances, the accuracy of remote heart rate monitoring technology has been significantly improved. Despite of the significant algorithmic advances, the performance of rPPG algorithm can degrade in the long-term, high-intensity continuous work occurred in evenings or insufficient light environments. One of the main challenges is that the lost facial details and low contrast cause the failure of detection and tracking. Also, insufficient lighting in video capturing hurts the quality of physiological signal. In this paper, we c…
Video-based Heart Rate Estimation with Angle-guided ROI Optimization and Graph Signal Denoising
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University（华东师范大学上海市多维度信息处理重点实验室）；Shanghai Changzheng Hospital, Naval Military Medical University（海军军医大学上海长征医院）；Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University（华东师范大学重庆研究院重庆市精密光学重点实验室）
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Rostock University Medical Center（罗斯托克大学医学中心）；Charité-Universitätsmedizin Berlin（柏林夏里特医学院）
This paper presents an approach to assess the perfusion of visible human tissue from RGB video files. We propose metrics derived from remote photoplethysmography (rPPG) signals to detect whether a tissue is adequately supplied with blood. The perfusion analysis is done in three different scales, offering a flexible approach for different applications. We perform a plane-orthogonal-to-skin rPPG independently for locally defined regions of interest on each scale. From the extracted signals, we derive the signal-to-noise ratio, magnitude in the frequency domain, heart rate, perfusion index as wel…
DD-rPPGNet: De-interfering and Descriptive Feature Learning for Unsupervised rPPG Estimation
College of Computer and Cyber Security, Fujian Normal University（福建师范大学计算机与网络安全学院）；Department of Computer Science, National Tsing Hua University（国立清华大学计算机科学系）
Fudan University（复旦大学）；China University of Petroleum（中国石油大学）
Non-contact facial video-based heart rate estimation using remote photoplethysmography (rPPG) has shown great potential in many applications (e.g., remote health care) and achieved creditable results in constrained scenarios. However, practical applications require results to be accurate even under complex environment with head movement and unstable illumination. Therefore, improving the performance of rPPG in complex environment has become a key challenge. In this paper, we propose a novel video embedding method that embeds each facial video sequence into a feature map referred to as Multi-sc…
Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) estimation from facial videos which gives significant convenience compared with traditional contact-based measurements. In the real-world long-term health monitoring scenario, the distance of the participants and their head movements usually vary by time, resulting in the inaccurate rPPG measurement due to the varying face resolution and complex motion artifacts. Different from the previous rPPG models designed for a constant distance between camera and participants, in this paper, we propose two plug-and-play blocks (i.e.,…
The ability to reliably estimate physiological signals from video is a powerful tool in low-cost, pre-clinical health monitoring. In this work we propose a new approach to remote photoplethysmography (rPPG) - the measurement of blood volume changes from observations of a person's face or skin. Similar to current state-of-the-art methods for rPPG, we apply neural networks to learn deep representations with invariance to nuisance image variation. In contrast to such methods, we employ a fully self-supervised training approach, which has no reliance on expensive ground truth physiological trainin…
Semi-rPPG: Semi-Supervised Remote Physiological Measurement with Curriculum Pseudo-Labeling
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；School of Computing and Information Technology, Great Bay University（大湾区大学计算与信息技术学院）；Computer Vision Institute, School of Computer Science & Software Engineering, Shenzhen University（深圳大学计算机科学与技术学院计算机视觉研究所）
University of Oulu（奥卢大学）；University of Oxford（牛津大学）；Xi’an Jiaotong University（西安交通大学）；The University of Hong Kong（香港大学）
Remote photoplethysmography (rPPG), which aims at measuring heart activities and physiological signals from facial video without any contact, has great potential in many applications (e.g., remote healthcare and affective computing). Recent deep learning approaches focus on mining subtle rPPG clues using convolutional neural networks with limited spatio-temporal receptive fields, which neglect the long-range spatio-temporal perception and interaction for rPPG modeling. In this paper, we propose the PhysFormer, an end-to-end video transformer based architecture, to adaptively aggregate both loc…
Remote photoplethysmography (rPPG) allows for noncontact monitoring of blood volume changes from a camera by detecting minor fluctuations in reflected light. Prior applications of rPPG focused on face videos. In this paper we explored the feasibility of rPPG from non-face body regions such as the arms, legs, and hands. We collected a new dataset titled Multi-Site Physiological Monitoring (MSPM), which will be released with this paper. The dataset consists of 90 frames per second video of exposed arms, legs, and face, along with 10 synchronized PPG recordings. We performed baseline heart rate e…
We propose an end-to-end framework to measure people's vital signs including Heart Rate (HR), Heart Rate Variability (HRV), Oxygen Saturation (SpO2) and Blood Pressure (BP) based on the rPPG methodology from the video of a user's face captured with a smartphone camera. We extract face landmarks with a deep learning-based neural network model in real-time. Multiple face patches also called Regions-of-Interest (RoIs) are extracted by using the predicted face landmarks. Several filters are applied to reduce the noise from the RoIs in the extracted cardiac signals called Blood Volume Pulse (BVP) s…
PhysFlow: Frequency Decoupled with Dual-Field Rectified Flow for Remote Photoplethysmography
PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院教育部高维信息智能感知与系统重点实验室PCA实验室）；College of Computer Science and Technology, Qingdao University（青岛大学计算机科学技术学院）
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Great Bay University（大湾区大学）；Hefei University of Technology（合肥工业大学）；Macao Polytechnic University（澳门理工学院）
Joint Spatial-Temporal Modeling and Contrastive Learning for Self-supervised Heart Rate Measurement
School of Artificial Intelligence, Hefei University of Technology (HFUT)（合肥工业大学人工智能学院）；Key Laboratory of Knowledge Engineering with Big Data (HFUT), Ministry of Education（大数据知识工程教育部重点实验室）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；Anhui University（安徽大学）；Zhejiang University（浙江大学）；Anhui Zhonghuitong Technology Co., Ltd.（安徽中汇通科技有限公司）
With a surge in online medical advising remote monitoring of patient vitals is required. This can be facilitated with the Remote Photoplethysmography (rPPG) techniques that compute vital signs from facial videos. It involves processing video frames to obtain skin pixels, extracting the cardiac data from it and applying signal processing filters to extract the Blood Volume Pulse (BVP) signal. Different algorithms are applied to the BVP signal to estimate the various vital signs. We implemented a web application framework to measure a person's Heart Rate (HR), Heart Rate Variability (HRV), Oxyge…
Camera-Based HRV Prediction for Remote Learning Environments
Central China Normal University（华中师范大学）；National Key Laboratory of Human Factors Engineering（人因工程国家重点实验室）；Tsinghua University（清华大学）；East China Normal University（华东师范大学）
Dual-path TokenLearner for Remote Photoplethysmography-based Physiological Measurement with Facial Videos
Hefei University of Technology（合肥工业大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；The 38th Research Institute of China Electronics Technology Group Corporation（中国电子科技集团公司第三十八研究所）
