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IAPR Workshop on Multimodal Pattern Recognition of Social Signals in Human-Computer Interaction

MPRSS 2016: Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction pp 60–72Cite as

Performance Analysis of Gesture Recognition Classifiers for Building a Human Robot Interface

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10183))

Abstract

In this paper we present a natural human computer interface based on gesture recognition. The principal aim is to study how different personalized gestures, defined by users, can be represented in terms of features and can be modelled by classification approaches in order to obtain the best performances in gesture recognition. Ten different gestures involving the movement of the left arm are performed by different users. Different classification methodologies (SVM, HMM, NN, and DTW) are compared and their performances and limitations are discussed. An ensemble of classifiers is proposed to produce more favorable results compared to those of a single classifier system. The problems concerning different lengths of gesture executions, variability in their representations, generalization ability of the classifiers have been analyzed and a valuable insight in possible recommendation is provided.

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Authors and Affiliations

  1. Institute of Intelligent Systems for Automation (ISSIA), National Research Council of Italy, Bari, Italy

    Tiziana D’Orazio, Nicola Mosca, Roberto Marani & Grazia Cicirelli

Authors
  1. Tiziana D’Orazio
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  2. Nicola Mosca
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  3. Roberto Marani
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  4. Grazia Cicirelli
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Corresponding author

Correspondence to Grazia Cicirelli .

Editor information

Editors and Affiliations

  1. Universität Ulm, Ulm, Germany

    Friedhelm Schwenker

  2. Multimodal Communication and Computation, University of Southern California, Playa Vista, California, USA

    Stefan Scherer

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D’Orazio, T., Mosca, N., Marani, R., Cicirelli, G. (2017). Performance Analysis of Gesture Recognition Classifiers for Building a Human Robot Interface. In: Schwenker, F., Scherer, S. (eds) Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction. MPRSS 2016. Lecture Notes in Computer Science(), vol 10183. Springer, Cham. https://doi.org/10.1007/978-3-319-59259-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-59259-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59258-9

  • Online ISBN: 978-3-319-59259-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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