FSSGR: Feature Selection System to Dynamic Gesture Recognition

  • Diego G. S. Santos
  • Rodrigo C. Neto
  • Bruno Fernandes
  • Byron BezerraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)


Dynamic gesture recognition systems based on computer vision techniques have been frequently used in some fields such as medical, games and sign language. Usually, these systems have a time execution problem caused by the elevated number of features or attributes extracted for gesture classification. This work presents a system for dynamic gesture recognition that uses Particle Swarm Optimization to reduce the feature vector while increases the classification capability. The system FSSGR, Feature Selection System to Dynamic Gesture Recognition, solved the gesture recognition problem in RPPDI dataset, achieving 99.21% of classification rate with the same vectors size of previous works on the same database, although with a better response time.


Particle Swarm Optimization Feature Vector Gesture Recognition Smart City Hand Gesture Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Diego G. S. Santos
    • 1
  • Rodrigo C. Neto
    • 1
  • Bruno Fernandes
    • 1
  • Byron Bezerra
    • 1
    Email author
  1. 1.Escola Politécnica de Pernambuco - Universidade de PernambucoRecifeBrazil

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