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.
Originalsprog | Engelsk |
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Titel | IEEE International Conference on Image Processing Theory, Tools and Applications |
Forlag | IEEE |
Publikationsdato | dec. 2016 |
ISBN (Trykt) | 978-1-4673-8911-2 |
ISBN (Elektronisk) | 978-1-4673-8910-5 |
DOI | |
Status | Udgivet - dec. 2016 |
Begivenhed | IEEE International Conference on Image Processing Theory, Tools and Applications - Oulu, Finland Varighed: 12 dec. 2016 → 15 dec. 2016 Konferencens nummer: 6 http://www.ipta-conference.com/ipta16/ |
Konference
Konference | IEEE International Conference on Image Processing Theory, Tools and Applications |
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Nummer | 6 |
Land/Område | Finland |
By | Oulu |
Periode | 12/12/2016 → 15/12/2016 |
Internetadresse |