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Authors: Paulo Trigueiros 1 ; Fernando Ribeiro 2 and Luis Paulo Reis 3

Affiliations: 1 University of Minho, Portugal ; 2 Universidade do Minho, Portugal ; 3 University of Minho / LIACC, Portugal

Keyword(s): Hand Gesture Recognition, Machine Vision, Hand Features, Hog, Fourier Descriptors, Centroid Distance, Radial Signature, Shi-Tomasi Corner Detection.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Intelligent User Interfaces ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Symbolic Systems ; Vision and Perception

Abstract: 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 sep arately obtain better results, being at the same time simple in terms of computational complexity. (More)

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Paper citation in several formats:
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; ISSN 2184-433X, pages 51-61. DOI: 10.5220/0004200100510061

@conference{icaart13,
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,},
year={2013},
pages={51-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004200100510061},
isbn={978-989-8565-39-6},
issn={2184-433X},
}

TY - CONF

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
IS - 2184-433X
AU - Trigueiros, P.
AU - Ribeiro, F.
AU - Reis, L.
PY - 2013
SP - 51
EP - 61
DO - 10.5220/0004200100510061