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A synthetic training framework for providing gesture scalability to 2.5D pose-based hand gesture recognition systems

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Abstract

The use of hand gestures offers an alternative to the commonly used human computer interfaces (i.e., keyboard, mouse, gamepad), providing a more intuitive way of navigating among menus and in multimedia applications. One of the most difficult issues when designing a hand gesture recognition system is to introduce new detectable gestures without high cost, this is known as gesture scalability. Commonly, the introduction of new gestures needs a recording session of them, involving real subjects in the process. This paper presents a training framework for hand posture detection systems based on a learning scheme fed with synthetically generated range images. Different configurations of a 3D hand model result in sets of synthetic subjects, which have shown good performance in the separation of gestures from several dictionaries of the State of Art. The proposed approach allows the learning of new dictionaries with no need of recording real subjects, so it is fully scalable in terms of gestures. The obtained accuracy rates for the dictionaries evaluated are comparable to, and for some cases better than, the ones reported for different real subjects training schemes.

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Notes

  1. http://wii.com.

  2. http://www.xbox.com/kinect/.

  3. http://playstation.com/psmove/.

  4. http://www.mesa-imaging.ch/.

  5. http://www-vpu.eps.uam.es/DS/HGds.

  6. http://www.sematos.eu/lse.html.

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Authors and Affiliations

  1. Video Processing and Understanding Lab Laboratorio C-111, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Avda. Francisco Tomás y Valiente, 11, Madrid, Spain

    Javier Molina & José M. Martínez

  2. Ciudad Universitaria de Cantoblanco, Ctra. de Colmenar Viejo, km 15, 28049 , Madrid, Spain

    Javier Molina & José M. Martínez

Authors
  1. Javier Molina
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  2. José M. Martínez
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Correspondence to Javier Molina.

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Molina, J., Martínez, J.M. A synthetic training framework for providing gesture scalability to 2.5D pose-based hand gesture recognition systems. Machine Vision and Applications 25, 1309–1315 (2014). https://doi.org/10.1007/s00138-014-0620-7

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  • DOI: https://doi.org/10.1007/s00138-014-0620-7

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