Non-linear Distance-based Semi-supervised Multi-class Gesture Recognition

Husam Al-Behadili, Arne Grumpe, Christian Wöhler

2016

Abstract

The automatic recognition of gestures is important in a variety of applications, e.g. human-machine-interaction. Commonly, different individuals execute gestures in a slightly different manner and thus a fully labelled dataset is not available while unlabelled data may be acquired from an on-line stream. Consequently, gesture recognition systems should be able to be trained in a semi-supervised learning scenario. Additionally, real-time systems and large-scale data require a dimensionality reduction of the data to reduce the processing time. This is commonly achieved by linear subspace projections. Most of the gesture data sets, however, are non-linearly distributed. Hence, linear sub-space projection fails to separate the classes. We propose an extension to linear subspace projection by applying a non-linear transformation to a space of higher dimensional after the linear subspace projection. This mapping, however, is not explicitly evaluated but implicitly used by a kernel function. The kernel nearest class mean (KNCM) classifier is shown to handle the non-linearity as well as the semi-supervised learning scenario. The computational expense of the non-linear kernel function is compensated by the dimensionality reduction of the previous linear subspace projection. The method is applied to a gesture dataset comprised of 3D trajectories. The trajectories were acquired using the Kinect sensor. The results of the semi-supervised learning show high accuracies that approach the accuracy of a fully supervised scenario already for small dimensions of the subspace and small training sets. The accuracy of the semi-supervised KNCM exceeds the accuracy of the original nearest class mean classifier in all cases.

Paper Citation

in Harvard Style

Al-Behadili H., Grumpe A. and Wöhler C. (2016). Non-linear Distance-based Semi-supervised Multi-class Gesture Recognition . 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 280-286. DOI: 10.5220/0005674102800286

in Bibtex Style

@conference{visapp16,
author={Husam Al-Behadili and Arne Grumpe and Christian Wöhler},
title={Non-linear Distance-based Semi-supervised Multi-class Gesture Recognition},
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={280-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005674102800286},
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 - Non-linear Distance-based Semi-supervised Multi-class Gesture Recognition
SN - 978-989-758-175-5
AU - Al-Behadili H.
AU - Grumpe A.
AU - Wöhler C.
PY - 2016
SP - 280
EP - 286
DO - 10.5220/0005674102800286