Using Time Proportionate Intensity Images with Non-linear Classifiers for Hand Gesture Recognition

  • Omar AhmadEmail author
  • Basilio Bona
  • Muhammad Latif Anjum
  • Ikramullah Khosa
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 291)


Gestures are signals that contain important spatiotemporal information. Understanding gestures is a trivial task for humans, but for machines it is a challenging task involving thousands of computations per video frame. This paper investigates an efficient hand gesture recognition technique which is based on time projections of the hand location. For recognition, non-linear classifiers, namely Support Vector Machines and Artificial Neural Networks, are tested. The proposed method performs much faster than the conventional Markov Model based gesture recognition techniques while achieving comparable recognition results.


Gesture recognition Spatiotemporal segmentation Computer vision Machine learning Classification Human robot interaction 


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Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Omar Ahmad
    • 1
    Email author
  • Basilio Bona
    • 2
  • Muhammad Latif Anjum
    • 1
  • Ikramullah Khosa
    • 3
  1. 1.Department of Mechanical and Aerospace Engineering (DIMEAS)Politecnico di TorinoTurinItaly
  2. 2.Department of Control and Computer Engineering (DAUIN)Politecnico di TorinoTurinItaly
  3. 3.Department of Electronics and Telecommunications (DET)Politecnico di TorinoTurinItaly

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