Thermal Super-Pixels for Bimodal Stress Recognition

Publikation: Forskning - peer reviewKonferenceartikel i proceeding

Abstrakt

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.
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Detaljer

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
UdgiverIEEE
Publikationsdatodec. 2016
ISBN (trykt)978-1-4673-8911-2
ISBN (elektronisk)978-1-4673-8910-5
DOI
StatusUdgivet - dec. 2016
Begivenhed - Oulu, Finland

Konference

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