Spatiotemporal Analysis of RGB-D-T Facial Images for Multimodal Pain Level Recognition
Abstract
Pain is a vital sign of human health and its automatic detection<br/>can be of crucial importance in many different contexts,<br/>including medical scenarios. While most available<br/>computer vision techniques are based on RGB, in this paper,<br/>we investigate the effect of combining RGB, depth, and thermal<br/>facial images for pain detection and pain intensity level<br/>recognition. For this purpose, we extract energies released<br/>by facial pixels using a spatiotemporal filter. Experiments<br/>on a group of 12 elderly people applying the multimodal approach<br/>show that the proposed method successfully detects<br/>pain and recognizes between three intensity levels in 82%<br/>of the analyzed frames improving more than 6% over RGB<br/>only analysis in similar conditions.
Cite
Text
Irani et al. "Spatiotemporal Analysis of RGB-D-T Facial Images for Multimodal Pain Level Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015. doi:10.1109/CVPRW.2015.7301341Markdown
[Irani et al. "Spatiotemporal Analysis of RGB-D-T Facial Images for Multimodal Pain Level Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015.](https://mlanthology.org/cvprw/2015/irani2015cvprw-spatiotemporal/) doi:10.1109/CVPRW.2015.7301341BibTeX
@inproceedings{irani2015cvprw-spatiotemporal,
title = {{Spatiotemporal Analysis of RGB-D-T Facial Images for Multimodal Pain Level Recognition}},
author = {Irani, Ramin and Nasrollahi, Kamal and Simon, Marc Oliu and Corneanu, Ciprian A. and Escalera, Sergio and Bahnsen, Chris and Lundtoft, Dennis H. and Moeslund, Thomas B. and Pedersen, Tanja L. and Klitgaard, Maria-Louise and Petrini, Laura},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2015},
pages = {88-95},
doi = {10.1109/CVPRW.2015.7301341},
url = {https://mlanthology.org/cvprw/2015/irani2015cvprw-spatiotemporal/}
}