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Deep learning in vision-based static hand gesture recognition

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Abstract

Hand gesture for communication has proven effective for humans, and active research is ongoing in replicating the same success in computer vision systems. Human–computer interaction can be significantly improved from advances in systems that are capable of recognizing different hand gestures. In contrast to many earlier works, which consider the recognition of significantly differentiable hand gestures, and therefore often selecting a few gestures from the American Sign Language (ASL) for recognition, we propose applying deep learning to the problem of hand gesture recognition for the whole 24 hand gestures obtained from the Thomas Moeslund’s gesture recognition database. We show that more biologically inspired and deep neural networks such as convolutional neural network and stacked denoising autoencoder are capable of learning the complex hand gesture classification task with lower error rates. The considered networks are trained and tested on data obtained from the above-mentioned public database; results comparison is then made against earlier works in which only small subsets of the ASL hand gestures are considered for recognition.

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Authors and Affiliations

  1. European Centre for Research and Academic Affairs (ECRAA), Lefkosa, Mersin-10, Northern Cyprus, Turkey

    Oyebade K. Oyedotun & Adnan Khashman

  2. University of Kyrenia, Karakum, Kyrenia, Mersin-10, Northern Cyprus, Turkey

    Adnan Khashman

Authors
  1. Oyebade K. Oyedotun
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  2. Adnan Khashman
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Correspondence to Oyebade K. Oyedotun.

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Oyedotun, O.K., Khashman, A. Deep learning in vision-based static hand gesture recognition. Neural Comput & Applic 28, 3941–3951 (2017). https://doi.org/10.1007/s00521-016-2294-8

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  • DOI: https://doi.org/10.1007/s00521-016-2294-8

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