Using clustering techniques for intelligent camera-based user interfaces

Bankovic, Zorana and Moya Fernández, José Manuel and Romero Perales, Elena and Blesa Martínez, Javier and Fraga Aydillo, David and Vallejo López, Juan Carlos and Araujo Pinto, Álvaro and Malagón Marzo, Pedro José and Goyeneche, Juan Mariano de and Villanueva González, Daniel and Nieto-Taladriz García, Octavio (2012). Using clustering techniques for intelligent camera-based user interfaces. "Logic Journal of the IGPL", v. 20 (n. 3); pp. 589-597. ISSN 1367-0751.


Title: Using clustering techniques for intelligent camera-based user interfaces
  • Bankovic, Zorana
  • Moya Fernández, José Manuel
  • Romero Perales, Elena
  • Blesa Martínez, Javier
  • Fraga Aydillo, David
  • Vallejo López, Juan Carlos
  • Araujo Pinto, Álvaro
  • Malagón Marzo, Pedro José
  • Goyeneche, Juan Mariano de
  • Villanueva González, Daniel
  • Nieto-Taladriz García, Octavio
Item Type: Article
Título de Revista/Publicación: Logic Journal of the IGPL
Date: June 2012
Volume: 20
Freetext Keywords: Gesture recognition, intelligent environments, self-organizing maps, unsupervised genetic algorithm
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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The area of Human-Machine Interface is growing fast due to its high importance in all technological systems. The basic idea behind designing human-machine interfaces is to enrich the communication with the technology in a natural and easy way. Gesture interfaces are a good example of transparent interfaces. Such interfaces must identify properly the action the user wants to perform, so the proper gesture recognition is of the highest importance. However, most of the systems based on gesture recognition use complex methods requiring high-resource devices. In this work, we propose to model gestures capturing their temporal properties, which significantly reduce storage requirements, and use clustering techniques, namely self-organizing maps and unsupervised genetic algorithm, for their classification. We further propose to train a certain number of algorithms with different parameters and combine their decision using majority voting in order to decrease the false positive rate. The main advantage of the approach is its simplicity, which enables the implementation using devices with limited resources, and therefore low cost. The testing results demonstrate its high potential.

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Item ID: 16821
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Deposited by: Memoria Investigacion
Deposited on: 11 Aug 2013 08:42
Last Modified: 21 Apr 2016 17:10
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