The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Remote Heart Rate Measurement from Face Videos under Realistic Situations
Abstract
Heart rate is an important indicator of people's physiological state. Recently, several papers reported methods to measure heart rate remotely from face videos. Those methods work well on stationary subjects under well controlled conditions, but their performance significantly degrades if the videos are recorded under more challenging conditions, specifically when subjects' motions and illumination variations are involved. We propose a framework which utilizes face tracking and Normalized Least Mean Square adaptive filtering methods to counter their influences. We test our framework on a large difficult and public database MAHNOB-HCI and demonstrate that our method substantially outperforms all previous methods. We also use our method for long term heart rate monitoring in a game evaluation scenario and achieve promising results.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Shi, J., & Tomasi (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323794 · Cited by 7,724 · cited 1×
“[15], and are tracked through the following frames using the Kanade-Lucas-Tomasi (KLT) algorithm [19].”
Method
Cited by
Algorithmic principles of remote PPG
Wang, W., den Brinker, A. C., Stuijk, S., & de Haan, G. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2609282 · Cited by 1,234
DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks
Chen, W., & McDuff, D. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01216-8_22 · Cited by 716
Photoplethysmogram Analysis and Applications: An Integrative Review
Park, J., Seok, H. S., Kim, S.-S., & Shin, H. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2021.808451 · Cited by 422
RhythmNet: End-to-End Heart Rate Estimation From Face via Spatial-Temporal Representation
Niu, X., Shan, S., Han, H., & Chen, X. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2947204 · Cited by 420
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
Qi, H., Guo, Q., Juefei-Xu, F., Xie, X., Ma, L., Feng, W., Liu, Y., & Zhao, J. (2020) · arXiv · doi:10.48550/arxiv.2006.07634 · Cited by 280
Metadata
- Authors
- Li, X., Chen, J., Zhao, G., & Pietikainen, M.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 4264-4271
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1986273245
- Cited by
- 550
- References
- 1
- FWCI
- 52.69
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Remote Heart Rate Measurement from Face Videos under Realistic Situations. https://app.cochat.ai/papers/10.1109/cvpr.2014.543
- The paper
- Li, X., Chen, J., Zhao, G., & Pietikainen, M. (2014). Remote Heart Rate Measurement from Face Videos under Realistic Situations. 2014 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2014.543
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
