Thermal Super-Pixels for Bimodal Stress Recognition

Ramin Irani, Kamal Nasrollahi, Abhinav Dhall, Thomas B. Moeslund, Tom Gedeon

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

5 Citationer (Scopus)
309 Downloads (Pure)

Resumé

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.
OriginalsprogEngelsk
TitelIEEE International Conference on Image Processing Theory, Tools and Applications
ForlagIEEE
Publikationsdatodec. 2016
ISBN (Trykt)978-1-4673-8911-2
ISBN (Elektronisk)978-1-4673-8910-5
DOI
StatusUdgivet - dec. 2016
BegivenhedIEEE International Conference on Image Processing Theory, Tools and Applications - Oulu, Finland
Varighed: 12 dec. 201615 dec. 2016
Konferencens nummer: 6
http://www.ipta-conference.com/ipta16/

Konference

KonferenceIEEE International Conference on Image Processing Theory, Tools and Applications
Nummer6
LandFinland
ByOulu
Periode12/12/201615/12/2016
Internetadresse

Fingerprint

Pixels
Fusion reactions
Cameras
Hot Temperature
Health
Monitoring
Sensors

Citer dette

Irani, R., Nasrollahi, K., Dhall, A., Moeslund, T. B., & Gedeon, T. (2016). Thermal Super-Pixels for Bimodal Stress Recognition. I IEEE International Conference on Image Processing Theory, Tools and Applications IEEE. https://doi.org/10.1109/IPTA.2016.7821002
Irani, Ramin ; Nasrollahi, Kamal ; Dhall, Abhinav ; Moeslund, Thomas B. ; Gedeon, Tom. / Thermal Super-Pixels for Bimodal Stress Recognition. IEEE International Conference on Image Processing Theory, Tools and Applications. IEEE, 2016.
@inproceedings{b1a997e831134e68babf73a873efef4c,
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 = "Stress Recognition, Facial Expression, RGB Images, Thermal Images, super-pixels",
author = "Ramin Irani and Kamal Nasrollahi and Abhinav Dhall and Moeslund, {Thomas B.} and Tom Gedeon",
year = "2016",
month = "12",
doi = "10.1109/IPTA.2016.7821002",
language = "English",
isbn = "978-1-4673-8911-2",
booktitle = "IEEE International Conference on Image Processing Theory, Tools and Applications",
publisher = "IEEE",
address = "United States",

}

Irani, R, Nasrollahi, K, Dhall, A, Moeslund, TB & Gedeon, T 2016, Thermal Super-Pixels for Bimodal Stress Recognition. i IEEE International Conference on Image Processing Theory, Tools and Applications. IEEE, IEEE International Conference on Image Processing Theory, Tools and Applications, Oulu, Finland, 12/12/2016. https://doi.org/10.1109/IPTA.2016.7821002

Thermal Super-Pixels for Bimodal Stress Recognition. / Irani, Ramin; Nasrollahi, Kamal; Dhall, Abhinav; Moeslund, Thomas B.; Gedeon, Tom.

IEEE International Conference on Image Processing Theory, Tools and Applications. IEEE, 2016.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

T1 - Thermal Super-Pixels for Bimodal Stress Recognition

AU - Irani, Ramin

AU - Nasrollahi, Kamal

AU - Dhall, Abhinav

AU - Moeslund, Thomas B.

AU - Gedeon, Tom

PY - 2016/12

Y1 - 2016/12

N2 - 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.

AB - 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.

KW - Stress Recognition

KW - Facial Expression

KW - RGB Images

KW - Thermal Images

KW - super-pixels

UR - http://www.ipta-conference.com/ipta16/images/IPTA_program_Final_Online.pdf

U2 - 10.1109/IPTA.2016.7821002

DO - 10.1109/IPTA.2016.7821002

M3 - Article in proceeding

SN - 978-1-4673-8911-2

BT - IEEE International Conference on Image Processing Theory, Tools and Applications

PB - IEEE

ER -

Irani R, Nasrollahi K, Dhall A, Moeslund TB, Gedeon T. Thermal Super-Pixels for Bimodal Stress Recognition. I IEEE International Conference on Image Processing Theory, Tools and Applications. IEEE. 2016 https://doi.org/10.1109/IPTA.2016.7821002