mlsanityData to 14 Oct 2026

Perfusion assessment via local remote photoplethysmography (rPPG)

arXiv 2208.13840 ↗First posted 29 Aug 2022cs.CV cs.LG eess.IVcited by 4

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Abstract (arXiv)

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 well as correlation between specific rPPG signals in order to locally assess the perfusion of a specific region of human tissue. We show that locally resolved rPPG has a broad range of applications. As exemplary applications, we present results in intraoperative perfusion analysis and visualization during skin and organ transplantation as well as an application for liveliness assessment for the detection of presentation attacks to authentication systems.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 6 of its 43 references that are in the map. First posted on arXiv 29 Aug 2022.

Builds on

6 of 43 references.

References · 6

  1. 2018DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networkscited by 154
  2. 2019Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networkscited by 128
  3. 2019Remote Heart Rate Measurement from Highly Compressed Facial Videos: an End-to-end Deep Learning Solution with Video Enhancementcited by 87
  4. 2021TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detectioncited by 41
  5. 2021The First Vision For Vitals (V4V) Challenge for Non-Contact Video-Based Physiological Estimationcited by 19
  6. 2021Assessment of Deep Learning-based Heart Rate Estimation using Remote Photoplethysmography under Different Illuminationscited by 6

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1.

4 papers.