Emotion Recognition through Body Language using RGB-D Sensor

Lilita Kiforenko, Dirk Kraft

2016

Abstract

This paper presents results on automatic non-acted human emotion recognition using full standing body movements and postures. The focus of this paper is to show that it is possible to classify emotions using a consumer depth sensor in an everyday scenario. The features for classification are body joint rotation angles and meta-features that are fed into a Support Vector Machines classifier. The work of Gaber-Barron and Si (2012) is used as inspiration and many of their proposed meta-features are reimplemented or modified. In this work we try to identify ”basic” human emotions, that are triggered by various visual stimuli. We present the emotion dataset that is recorded using Microsoft Kinect for Windows sensor and body joints rotation angles that are extracted using Microsoft Kinect Software Development Kit 1.6. The classified emotions are curiosity, confusion, joy, boredom and disgust. We show that human real emotions can be classified using body movements and postures with a classification accuracy of 55.62%.

Paper Citation

in Harvard Style

Kiforenko L. and Kraft D. (2016). Emotion Recognition through Body Language using RGB-D Sensor . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 398-405. DOI: 10.5220/0005783403980405

in Bibtex Style

@conference{visapp16,
author={Lilita Kiforenko and Dirk Kraft},
title={Emotion Recognition through Body Language using RGB-D Sensor},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={398-405},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005783403980405},
isbn={978-989-758-175-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Emotion Recognition through Body Language using RGB-D Sensor
SN - 978-989-758-175-5
AU - Kiforenko L.
AU - Kraft D.
PY - 2016
SP - 398
EP - 405
DO - 10.5220/0005783403980405