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View-invariant gesture recognition using nonparametric shape descriptor

  • Beihang University
  • University of Bergamo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we propose a new method for view-invariant gesture recognition, based on what we call nonparametric shape descriptor. We represent gestures as 3D motion trajectories and then we prove that the shape of a trajectory is equivalent to the Euclidean distances between all its points. The set of point-to-point distances description is mapped to a high-dimensional kernel space by kernel principal component analysis (KPCA), and then nonparametric discriminant analysis (NDA) is used to extract the view-invariant shape features as the input for pattern classification. The algorithm is performed on a public dataset, and shows better view-invariant performance than other state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages544-549
Number of pages6
ISBN (Electronic)9781479952083
DOIs
StatePublished - 4 Dec 2014
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference22nd International Conference on Pattern Recognition, ICPR 2014
Country/TerritorySweden
CityStockholm
Period24/08/1428/08/14

Keywords

  • Gesture and behavior analysis
  • Human computer interaction
  • Motion
  • Tracking and video analysis

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