Hand Gesture Recognition for Human Computer Interaction: A Comparative Study of Different Image Features

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2014

Autores

trigueiros, p (Autor) Outra; ribeiro, fr (Autor) Outra; reis, lp (Autor) FCUP

Ata de Conferência Internacional

Título: AGENTS AND ARTIFICIAL INTELLIGENCE, ICAART 2013

Páginas: 162-178

5th International Conference on Agents and Artificial Intelligence (ICAART)

Barcelona, SPAIN, FEB 15-18, 2013

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00A-2EX

DOI: 10.1007/978-3-662-44440-5_10

Abstract (EN): Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91 % and 90,1 % respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 17