@inproceedings{chen2018deepphys,
author = {Chen, Weixuan and McDuff, Daniel},
title = {DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks},
booktitle = {ECCV},
year = {2018},
month = {September},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/deepphys-video-based-physiological-measurement-using-convolutional-attention-networks/},
}