Feature selection for hand pose recognition in human-robot object exchange scenario

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dc.contributor.authorRasines, Irati
dc.contributor.authorRemazeilles, Anthony
dc.contributor.authorBengoa, Pedro M.Iriondo
dc.contributor.editorMartinez Garcia, Herminio
dc.contributor.editorGrau, Antoni
dc.contributor.institutionRobótica Médica
dc.date.accessioned2024-07-24T11:56:33Z
dc.date.available2024-07-24T11:56:33Z
dc.date.issued2014-01-08
dc.descriptionPublisher Copyright: © 2014 IEEE.
dc.description.abstractVision-based hand gesture recognition relies on the extraction of features describing the hand, and the appropriate set of features is usually selected in an empirical manner. We propose in this article a systematic selection of the best features to be considered. An iterative sequential forward feature selection (SFS) approach is proposed to combine the features with the highest recognition rate considering the Gaussian Mixture Modelling within the Expectation Maximization algorithm as classification technique. This approach has been tested with two different illustrative databases. The first one is related to human robot physical interaction and the hand postures considered correspond to key postures the human partner performs just before acquiring an object from the robot. The second database corresponds to the representation of the 10 first numbers of the American Sign Language. In both cases, the recognition rate obtained, measured through the F1 score metrics, is satisfactory (over 0,97), and demonstrates that the proposed technique could be applied to a very large field of applications.en
dc.description.statusPeer reviewed
dc.identifier.citationRasines , I , Remazeilles , A & Bengoa , P M I 2014 , Feature selection for hand pose recognition in human-robot object exchange scenario . in H Martinez Garcia & A Grau (eds) , 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014 . , 7005139 , 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014 , Institute of Electrical and Electronics Engineers Inc. , 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014 , Barcelona , Spain , 16/09/14 . https://doi.org/10.1109/ETFA.2014.7005139
dc.identifier.citationconference
dc.identifier.doi10.1109/ETFA.2014.7005139
dc.identifier.isbn9781479948468
dc.identifier.urihttps://hdl.handle.net/11556/2637
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=84946685486&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014
dc.relation.ispartofseries19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsFeature selection
dc.subject.keywordsGaussian Mixture Models
dc.subject.keywordsHuman-Robot interaction
dc.subject.keywordsSFS
dc.subject.keywordsVision-based hand static gesture recognition
dc.subject.keywordsControl and Systems Engineering
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsArtificial Intelligence
dc.titleFeature selection for hand pose recognition in human-robot object exchange scenarioen
dc.typeconference output
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