@inproceedings{c69789dd245e4c3cb8d0d7ed7b462e0a,
title = "Thermal super-pixels for bimodal stress recognition",
abstract = "Stress is a response to time pressure or negative environmental conditions. If its stimulus iterates or stays for a long time, it affects health conditions. Thus, stress recognition is an important issue. Traditional systems for this purpose are mostly contact-based, i.e., they require a sensor to be in touch with the body which is not always practical. Contact-free monitoring of the stress by a camera [1], [2] can be an alternative. These systems usually utilize only an RGB or a thermal camera to recognize stress. To the best of our knowledge, the only work on fusion of these two modalities for stress recognition is [3] which uses a feature level fusion of the two modalities. The features in [3] are extracted directly from pixel values. In this paper we show that extracting the features from super-pixels, followed by decision level fusion results in a system outperforming [3]. The experimental results on ANUstressDB database show that our system achieves 89% classification accuracy.",
keywords = "Facial Expression, RGB Images, Stress Recognition, Super-pixels, Thermal Images",
author = "Ramin Irani and Kamal Nasrollahi and Abhinav Dhall and Moeslund, {Thomas B.} and Tom Gedeon",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016 ; Conference date: 12-12-2016 Through 15-12-2016",
year = "2017",
month = jan,
day = "17",
doi = "10.1109/IPTA.2016.7821002",
language = "English",
series = "2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Matti Pietikainen and Abdenour Hadid and Lopez, {Miguel Bordallo}",
booktitle = "2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016",
address = "United States",
}