Remote photoplethysmography (rPPG), enabling non-contact physiological monitoring through facial light reflection analysis, faces critical computational bottlenecks as deep learning introduces performance gains at the cost of prohibitive resource demands. This paper proposes ME-rPPG, a memory-efficient algorithm built on temporal-spatial state space duality, which resolves the trilemma of model scalability, cross-dataset generalization, and real-time constraints. Leveraging a transferable state space, ME-rPPG efficiently captures subtle periodic variations across facial frames while maintaining minimal computational overhead, enabling training on extended video sequences and supporting low-latency inference. Achieving cross-dataset MAEs of 5.38 (MMPD), 0.70 (VitalVideo), and 0.25 (PURE), ME-rPPG outperforms all baselines with improvements ranging from 21.3% to 60.2%. Our solution enables real-time inference with only 3.6 MB memory usage and 9.46 ms latency -- surpassing existing methods by 19.5%-49.7% accuracy and 43.2% user satisfaction gains in real-world deployments. The code and demos are released for reproducibility on https://health-hci-group.github.io/ME-rPPG-demo/.
We present a lightweight neural model for remote heart rate estimation focused on the efficient spatio-temporal learning of facial photoplethysmography (PPG) based on i) modelling of PPG dynamics by combinations of multiple convolutional derivatives, and ii) increased flexibility of the model to learn possible offsets between the facial video PPG and the ground truth. PPG dynamics are modelled by a Temporal Derivative Module (TDM) constructed by the incremental aggregation of multiple convolutional derivatives, emulating a Taylor series expansion up to the desired order. Robustness to ground t…
3D mask face presentation attack detection (PAD) plays a vital role in securing face recognition systems from emergent 3D mask attacks. Recently, remote photoplethysmography (rPPG) has been developed as an intrinsic liveness clue for 3D mask PAD without relying on the mask appearance. However, the rPPG features for 3D mask PAD are still needed expert knowledge to design manually, which limits its further progress in the deep learning and big data era. In this letter, we propose a pure rPPG transformer (TransRPPG) framework for learning intrinsic liveness representation efficiently. At first, r…
Architectural improvements are studied for convolutional network performing estimation of heart rate (HR) values on color signal patches. Color signals are time series of color components averaged over facial regions recorded by webcams in two scenarios: Stationary (without motion of a person) and Mixed Motion (different motion patterns of a person). HR estimation problem is addressed as a classification task, where classes correspond to different heart rate values within the admissible range of [40; 125] bpm. Both adding convolutional filtering layers after fully connected layers and involvin…
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…
HOT: Harmonic-Constrained Optimal Transport for Remote Photoplethysmography Domain Adaptation
VNU University of Engineering and Technology, Hanoi, Vietnam（越南河内国立大学工程与技术大学）；VinUni-Illinois Smart Health Center, VinUniversity, Hanoi, Vietnam（VinUni-Illinois智慧健康中心，VinUniversity，越南河内）；College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam（VinUniversity工程与计算机科学学院，越南河内）；Center for Innovations in Health Sciences, VinUniversity, Hanoi, Vietnam（VinUniversity健康科学创新中心，越南河内）
FreqPhys: Repurposing Implicit Physiological Frequency Prior for Robust Remote Photoplethysmography
Hefei University of Technology（合肥工业大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；University of Science and Technology Beijing（北京科技大学）；Great Bay University（大湾区大学）
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）
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.,…
BTS-rPPG: Orthogonal Butterfly Temporal Shifting for Remote Photoplethysmography
VNU University of Engineering and Technology, Hanoi, Vietnam（越南河内国立大学工程与技术大学）；VinUni-Illinois Smart Health Center, VinUniversity, Hanoi, Vietnam（VinUni-Illinois智能健康中心，VinUniversity，越南河内）；College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam（VinUniversity工程与计算机科学学院，越南河内）；Center for Innovations in Health Sciences, VinUniversity, Hanoi, Vietnam（VinUniversity健康科学创新中心，越南河内）
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 …
Orientation-conditioned Facial Texture Mapping for Video-based Facial Remote Photoplethysmography Estimation
Australian National University（澳大利亚国立大学）；Data61, Commonwealth and Scientific Industrial Research Organization（联邦科学与工业研究组织Data61）；University of Turku（图尔库大学）
Remote Photoplethysmography (rPPG) is a fast-growing technique of vital sign estimation by analyzing video of a person. Several major phenomena affecting rPPG signals have been studied (e.g. video compression, distance from person to camera, skin tone, head motions). However, to develop a highly accurate rPPG method, new, minor, factors should be investigated. First considered factor is irregular frame rate of video recordings. Despite of PPG signal transformation by frame rate irregularity, no significant distortion of PPG signal spectra was found in the experiments. Second factor is rolling …
PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement
Great Bay University（大湾大学）；Hefei University of Technology（合肥工业大学）；Macao Polytechnic University（澳门 polytechnic 大学）；University of Science and Technology Beijing（北京科技大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳分校）；Dongguan Key Laboratory for Intelligence and Information Technology（东莞智能与信息科技重点实验室）
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）
MVRD-Bench: Multi-View Learning and Benchmarking for Dynamic Remote Photoplethysmography under Occlusion
Nanjing University of Information Science and Technology（南京信息工程大学）；University of Oulu（奥卢大学）；Xi’an Jiaotong University（西安交通大学）；Zhejiang University（浙江大学）
FCUS-rPPG: A Fast-Converging Unsupervised Framework for Remote Photoplethysmography via Gradient Oscillation Suppression
Department of Biomedical Engineering（生物医学工程系）；Anhui Province Key Laboratory of Measuring Theory and Precision Instrument（安徽省测量理论与精密仪器重点实验室）；Hefei University of Technology（合肥工业大学）；Department of Electronic Engineering and Information Science（电子工程与信息科学系）；University of Science and Technology of China（中国科学技术大学）
Measurement of the cardiac pulse from facial video has become an interesting pursuit of research over the last few years. This is mainly due to the increasing importance of obtaining the heart rate of an individual in a non-invasive manner, which can be highly useful for applications in gaming and the medical industry. Another instrumental area of research over the past few years has been the advent of Deep Learning and using Deep Neural networks to enhance task performance. In this work, we propose to use efficient convolutional networks to accurately measure the heart rate of user from low r…
Intervention-Based Self-Supervised Learning: A Causal Probe Paradigm for Remote Photoplethysmography
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, PR China（合肥工业大学计算机科学与信息工程学院）；College of Aviation Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan 618307, PR China（中国民航飞行大学航空电子电气工程学院）；School of Computing and Information Technology, Great Bay University, Dongguan 523000, PR China（广东大湾区大学计算机科学与信息工程学院）
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…
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…
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…
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）
University of Oulu（奥卢大学）；VTT Technical Research Centre of Finland Ltd（芬兰VTT技术研究中心有限公司）
Depression is a mental illness that may be harmful to an individual's health. The detection of mental health disorders in the early stages and a precise diagnosis are critical to avoid social, physiological, or psychological side effects. This work analyzes physiological signals to observe if different depressive states have a noticeable impact on the blood volume pulse (BVP) and the heart rate variability (HRV) response. Although typically, HRV features are calculated from biosignals obtained with contact-based sensors such as wearables, we propose instead a novel scheme that directly extract…
Biometric Authentication Based on Enhanced Remote Photoplethysmography Signal Morphology
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）；State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全国家重点实验室）
CanonicalPhys: Pose-Robust Remote Photoplethysmography via Canonical-Space Priors
Center for Machine Vision and Signal Analysis (CMVS), University of Oulu（奥卢大学机器视觉与信号分析中心）；ELLIS Institute Finland（芬兰ELLIS研究所）；Zhejiang University（浙江大学）
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…
Remote photoplethysmography (rPPG), a family of techniques for monitoring blood volume changes, may be especially useful for widespread contactless health monitoring using face video from consumer-grade visible-light cameras. The COVID-19 pandemic has caused the widespread use of protective face masks. We found that occlusions from cloth face masks increased the mean absolute error of heart rate estimation by more than 80\% when deploying methods designed on unmasked faces. We show that augmenting unmasked face videos by adding patterned synthetic face masks forces the model to attend to the p…
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…
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Camera-based remote photoplethysmography (rPPG) provides a non-contact way to measure physiological signals (e.g., heart rate) using facial videos. Recent deep learning architectures have improved the accuracy of such physiological measurement significantly, yet they are restricted by the diversity of the annotated videos. The existing datasets MMSE-HR, AFRL, and UBFC-RPPG contain roughly 10%, 0%, and 5% of dark-skinned subjects respectively. The unbalanced training sets result in a poor generalization capability to unseen subjects and lead to unwanted bias toward different demographic groups.…
The Hong Kong University of Science & Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science & Technology（香港科技大学）；Great Bay University（大湾区大学）；Kuaishou Technology（快手科技）
Remote photoplethysmography (rPPG) technology has drawn increasing attention in recent years. It can extract Blood Volume Pulse (BVP) from facial videos, making many applications like health monitoring and emotional analysis more accessible. However, as the BVP signal is easily affected by environmental changes, existing methods struggle to generalize well for unseen domains. In this paper, we systematically address the domain shift problem in the rPPG measurement task. We show that most domain generalization methods do not work well in this problem, as domain labels are ambiguous in complicat…
