Robust Pulse Rate From Chrominance-Based rPPG

By G. de Haan, Vincent Jeanne. IEEE Transactions on Biomedical Engineering, 2013

1,308 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/6de29d37f87e75b1d2742f8b1a0a2bffa6025128

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In Biomedical Engineering

In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

Citations in the corpus

FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals (2019, methodology): “Several approaches proposed improvements to PPG, using chrominance features [18], G channel components [61], optical properties [20], kalman ﬁlters [5], and different facial areas for signal extraction [61, 54, 20, 47].”
RhythmNet: End-to-End Heart Rate Estimation From Face via Spatial-Temporal Representation (2019, methodology): “method [4] shows minor performance degradation.”
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.”
DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks (2018, methodology): “We compare our proposed approach to six other methods [10, 19, 3, 13, 37, 34] for recovering the blood volume pulse.”
PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer (2021, neutral): “As shown in Table 1, all three traditional methods (Tulyakov2016 [55], POS [59] and CHROM [13]) perform poorly due to the complex scenarios (e.”
Algorithmic Principles of Remote PPG (2017, methodology): “The reason for this phenomenon has not been explained in [4], but we will show it later.”
Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement (2019, methodology): “, chrominancebased rPPG (CHROM) [4], projection plane orthogonal to the skin tone (POS) [35], and spatial subspace rotation [36, 34, 13].”
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision (2018, methodology): “[35] extract rPPG signals (also via [20]) from three face regions and two non-face regions, for detecting print and replay attacks.”
Photoplethysmography revisited: from contact to noncontact, from point to imaging (2015, neutral): “de Hann and Jeanne later introduced a chrominance-based method and showed that it is superior in SNR and motion robustness [99].”
Self-Adaptive Matrix Completion for Heart Rate Estimation from Face Videos under Realistic Conditions (2016, methodology): “On the other hand, the estimated low-rank matrix is enforced to resemble the observations.”
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (2020, methodology): “To help address these issues, some approaches incorporate prior knowledge about the physical properties of the patient’s skin [16, 17].”
Visual Heart Rate Estimation with Convolutional Neural Network (2018, methodology): “1 show that CHROM method yields the best MAE, but in case of the other measures, HR-CNN performs the best.”
A Novel Algorithm for Remote Photoplethysmography: Spatial Subspace Rotation (2016, methodology): “2508602 RGB traces into independent signal-sources using different criteria and takes the most periodic source as the pulse; 2) CHROM [5], which computes the pulse-signal as a linear combination of chrominance-signals assuming a standardized skin-color to”