Book Section #62413

Liu, Li and Shao, Ling (2013) UNSPECIFIED In: UNSPECIFIED UNSPECIFIED.

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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 synthesize machine learned 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 optimization 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.

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Pure Connector
Date Deposited: 10 Feb 2017 02:27
Last Modified: 25 Jul 2018 01:14
URI: https://ueaeprints.uea.ac.uk/id/eprint/62413
DOI: 10.1109/FG.2013.6553765

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