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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2013

Autores

trigueiros, p (Autor) Outra; ribeiro, f (Autor) Outra; reis, lp (Autor) FEUP

Ata de Conferência Internacional

Título: ICAART 2013 - Proceedings of the 5th International Conference on Agents and Artificial Intelligence

Páginas: 51-61

ICAART 2013 - Proceedings of the 5th International Conference on Agents and Artificial Intelligence, Volume 2, Barcelona, Spain, 15-18 February, 2013

Indexação

ISI Web of Knowledge

Scopus - 18 Citações

Outras Informações

ID Authenticus: P-008-AZF

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11