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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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