DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks
Abstract
Non-contact video-based physiological measurement has many applications in health care and human-computer interaction. Practical applications require measurements to be accurate even in the presence of large head rotations. We propose the first end-to-end system for video-based measurement of heart and breathing rate using a deep convolutional network. The system features a new motion representation based on a skin reflection model and a new attention mechanism using appearance information to guide motion estimation, both of which enable robust measurement under heterogeneous lighting and major motions. Our approach significantly outperforms all current state-of-the-art methods on both RGB and infrared video datasets. Furthermore, it allows spatial-temporal distributions of physiological signals to be visualized via the attention mechanism.
Cite
Text
Chen and McDuff. "DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01216-8_22Markdown
[Chen and McDuff. "DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/chen2018eccv-deepphys/) doi:10.1007/978-3-030-01216-8_22BibTeX
@inproceedings{chen2018eccv-deepphys,
title = {{DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks}},
author = {Chen, Weixuan and McDuff, Daniel},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2018},
doi = {10.1007/978-3-030-01216-8_22},
url = {https://mlanthology.org/eccv/2018/chen2018eccv-deepphys/}
}