DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks

Weixuan Chen; Daniel McDuff

ECCV 2018poster686 citations

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.

BibTeX

@inproceedings{eccv2018_deepphysvideobas,
  title = {DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks},
  author = {Weixuan Chen and Daniel McDuff},
  booktitle = {ECCV 2018},
  year = {2018}
}