Exploiting Silhouette Descriptors and Synthetic Data for Hand Gesture Recognition

Memo, Alvise; Minto, Ludovico; Zanuttigh, Pietro

Exploiting Silhouette Descriptors and Synthetic Data for Hand Gesture Recognition

Files

015-023.pdf (7.82 MB)

Date

2015

Authors

Memo, Alvise
Minto, Ludovico
Zanuttigh, Pietro

Publisher

The Eurographics Association

Abstract

This paper proposes a novel real-time hand gesture recognition scheme explicitly targeted to depth data. The hand silhouette is firstly extracted from the acquired data and then two ad-hoc feature sets are computed from this representation. The first is based on the local curvature of the hand contour, while the second represents the thickness of the hand region close to each contour point using a distance transform. The two feature sets are rearranged in a three dimensional data structure representing the values of the two features at each contour location and then this representation is fed into a multi-class Support Vector Machine. The classifier is trained on a synthetic dataset generated with an ad-hoc rendering system developed for the purposes of this work. This approach allows a fast construction of the training set without the need of manually acquiring large training datasets. Experimental results on real data show how the approach is able to achieve a 90% accuracy on a typical hand gesture recognition dataset with very limited computational resources.

        @inproceedings{10.2312:stag.20151288
,
booktitle = {Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference
},
editor = {Andrea Giachetti and Silvia Biasotti and Marco Tarini
},
title = {{Exploiting Silhouette Descriptors and Synthetic Data for Hand Gesture Recognition
}},
author = {Memo, Alvise and 
Minto, Ludovico and 
Zanuttigh, Pietro
},
year = {2015
},
publisher = {The Eurographics Association
},
ISBN = {978-3-905674-97-2
},
DOI = {10.2312/stag.20151288
}
}