A Comparative Study of Different Image Features for Hand Gesture Machine Learning

Paulo Trigueiros, Fernando Ribeiro, Luis Paulo Reis


Vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition. Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. In this paper we present a comparative study of seven different algorithms for hand feature extraction, for static hand gesture classification, analysed with RapidMiner in order to find the best learner. We defined our own gesture vocabulary, with 10 gestures, and we have recorded videos from 20 persons performing the gestures for later processing. Our goal in the present study is to learn features that, isolated, respond better in various situations in human-computer interaction. Results show that the radial signature and the centroid distance are the features that when used separately obtain better results, being at the same time simple in terms of computational complexity.


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Paper Citation

in Harvard Style

Trigueiros P., Ribeiro F. and Reis L. (2013). A Comparative Study of Different Image Features for Hand Gesture Machine Learning . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8565-39-6, pages 51-61. DOI: 10.5220/0004200100510061

in Bibtex Style

author={Paulo Trigueiros and Fernando Ribeiro and Luis Paulo Reis},
title={A Comparative Study of Different Image Features for Hand Gesture Machine Learning},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},

in EndNote Style

JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - A Comparative Study of Different Image Features for Hand Gesture Machine Learning
SN - 978-989-8565-39-6
AU - Trigueiros P.
AU - Ribeiro F.
AU - Reis L.
PY - 2013
SP - 51
EP - 61
DO - 10.5220/0004200100510061