University of Oulu

Sun, Z., Li, X. (2022). Contrast-Phys: Unsupervised Video-Based Remote Physiological Measurement via Spatiotemporal Contrast. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13672. Springer, Cham.

Contrast-Phys : unsupervised video-based remote physiological measurement via spatiotemporal contrast

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Author: Sun, Zhaodong1; Li, Xiaobai1
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: embargoed
Persistent link:
Language: English
Published: Springer Nature, 2022
Publish Date: 2023-10-23


Video-based remote physiological measurement utilizes face videos to measure the blood volume change signal, which is also called remote photoplethysmography (rPPG). Supervised methods for rPPG measurements achieve state-of-the-art performance. However, supervised rPPG methods require face videos and ground truth physiological signals for model training. In this paper, we propose an unsupervised rPPG measurement method that does not require ground truth signals for training. We use a 3DCNN model to generate multiple rPPG signals from each video in different spatiotemporal locations and train the model with a contrastive loss where rPPG signals from the same video are pulled together while those from different videos are pushed away. We test on five public datasets, including RGB videos and NIR videos. The results show that our method outperforms the previous unsupervised baseline and achieves accuracies very close to the current best supervised rPPG methods on all five datasets. Furthermore, we also demonstrate that our approach can run at a much faster speed and is more robust to noises than the previous unsupervised baseline. Our code is available at

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Series: Lecture notes in computer science
ISSN: 0302-9743
ISSN-E: 1611-3349
ISSN-L: 0302-9743
ISBN: 978-3-031-19775-8
ISBN Print: 978-3-031-19774-1
Volume: 13672
Pages: 492 - 510
DOI: 10.1007/978-3-031-19775-8_29
Host publication: Computer vision – ECCV 2022 : 17th European conference, Tel Aviv, Israel, October 23–27, 2022, proceedings, part XII
Host publication editor: Avidan, Shai
Brostow, Gabriel
Cissé, Moustapha
Farinella, Giovanni Maria
Hassner, Tal
Conference: European conference on computer vision
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: The study was supported by Academy of Finland (Project 323287 and 345948) and the Finnish Work Environment Fund (Project 200414). The authors also acknowledge CSC-IT Center for Science, Finland, for providing computational resources.
Academy of Finland Grant Number: 323287
Detailed Information: 323287 (Academy of Finland Funding decision)
345948 (Academy of Finland Funding decision)
Copyright information: © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: