Remote Heart Rate Measurement from Face Videos under Realistic Situations

By Xiaobai Li, Jie Chen, Guoying Zhao, Matti Pietikäinen. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

535 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/b503aa73b0c4de383a759e0345025a146277b81c

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In Biomedical Engineering

In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

Citations in the corpus

DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks (2018, methodology): “Color Space Transforms: The optical properties of the skin under ambient illumination mean that the green color channel tends to give the strongest PPG signal and this was used in initial work [35, 17].”
Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks (2019, methodology): “The first four [4, 10, 14, 22] are earlier methods not involving neural network.”
PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer (2021, unknown): “In earlier studies of facial rPPG measurement, most methods analyze subtle color changes on facial regions of interest (ROI) with classical signal processing approaches [29, 48, 49, 54, 56].”
Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement (2019, methodology): “In past few years, several traditional methods explored rPPG measurement from videos by analyzing subtle color changes on facial regions of interest (ROI), including blind source separation [19, 18], least mean square [12], majority voting [10] and self-adaptive matrix completion [31].”
RhythmNet: End-to-End Heart Rate Estimation From Face via Spatial-Temporal Representation (2019, methodology): “The baseline methods we use for comparisons are Poh2010 [1], Poh2011 [2], Balakrishnan2013 [3], Li2014 [5], Haan2013 [4] and Tulyakov2016 [6], and we directly use the results of these methods provided in [5] and [6].”
Self-Adaptive Matrix Completion for Heart Rate Estimation from Face Videos under Realistic Conditions (2016, methodology): “Long-time analysis constitutes a further limitation of existing works [17, 18, 19].”
Algorithmic Principles of Remote PPG (2017, methodology): “ing, such as the monitoring of heart rate [7]–[11], respiration [8], SpO2 [8], [12], blood pressure [13], neonates [14], [15], and the detection of atrial fibrillation [16] and mental”
Visual Heart Rate Estimation with Convolutional Neural Network (2018, methodology): “We test the proposed HR-CNN method on four datasets (standard: COHFACE, MAHNOB, PURE, newly collected: ECG-Fitness) against three baseline methods (LiCVPR [8], CHROM [4], 2SR [23]).”