Synthesis of spatio-temporal descriptors for dynamic hand gesture recognition using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Liu:2013:ieeeFG,
  author =       "Li Liu and Ling Shao",
  title =        "Synthesis of spatio-temporal descriptors for dynamic
                 hand gesture recognition using genetic programming",
  booktitle =    "10th IEEE International Conference and Workshops on
                 Automatic Face and Gesture Recognition (FG 2013)",
  year =         "2013",
  month =        "22-26 " # apr,
  keywords =     "genetic algorithms, genetic programming, gesture
                 recognition, learning (artificial intelligence),
                 Cambridge hand gesture dataset, Northwestern University
                 hand gesture dataset, automatic gesture recognition,
                 domain-independent optimisation, dynamic hand gesture
                 recognition, evolutionary method, machine learnt
                 spatio-temporal descriptors, Accuracy, Feature
                 extraction, Gabor filters, Gesture recognition, Support
                 vector machines, Training",
  DOI =          "doi:10.1109/FG.2013.6553765",
  abstract =     "Automatic gesture recognition has received much
                 attention due to its potential in various applications.
                 In this paper, we successfully apply an evolutionary
                 method-genetic programming (GP) to synthesise machine
                 learnt spatio-temporal descriptors for automatic
                 gesture recognition instead of using hand-crafted
                 descriptors. In our architecture, a set of primitive
                 low-level 3D operators are first randomly assembled as
                 tree-based combinations, which are further evolved
                 generation-by-generation through the GP system, and
                 finally a well performed combination will be selected
                 as the best descriptor for high-level gesture
                 recognition. To the best of our knowledge, this is the
                 first report of using GP to evolve spatio-temporal
                 descriptors for gesture recognition. We address this as
                 a domain-independent optimisation issue and evaluate
                 our proposed method, respectively, on two public
                 dynamic gesture datasets: Cambridge hand gesture
                 dataset and Northwestern University hand gesture
                 dataset to demonstrate its generalizability. The
                 experimental results manifest that our GP-evolved
                 descriptors can achieve better recognition accuracies
                 than state-of-the-art hand-crafted techniques.",
  notes =        "Also known as \cite{6553765}",
}

Genetic Programming entries for Li Liu Ling Shao

Citations