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Human Gait Recognition from Motion Capture Data in Signature Poses

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BALÁŽIA, Michal and Konstantinos N. PLATANIOTIS. Human Gait Recognition from Motion Capture Data in Signature Poses. IET Biometrics, London, UK: IET, 2017, vol. 6, No 2, p. 129-137. ISSN 2047-4938.
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Basic information
Original name Human Gait Recognition from Motion Capture Data in Signature Poses
Authors BALÁŽIA, Michal (703 Slovakia, guarantor, belonging to the institution) and Konstantinos N. PLATANIOTIS (124 Canada).
Edition IET Biometrics, London, UK, IET, 2017, 2047-4938.
Other information
Original language English
Type of outcome article in a journal
Field of Study Informatics
Country of publisher United Kingdom
Confidentiality degree is not subject to a state or trade secret
WWW URL URL URL
RIV identification code RIV/00216224:14330/17:00095906
Organization Fakulta informatiky - Repository
UT WoS 000396411600010
Keywords (in Czech) rozpoznavani podle chuze
Keywords in English gait recognition
Links MUNI/A/0915/2013. MUNI/A/1213/2014.
Changed by Changed by: RNDr. Daniel Jakubík, učo 139797. Changed: 9. 8. 2018 00:55.
Abstract
Most contribution to the field of structure-based human gait recognition has been done through design of extraordinary gait features. Many research groups that address this topic introduce a unique combination of gait features, select a couple of well-known object classiers, and test some variations of their methods on their custom Kinect databases. For a practical system, it is not necessary to invent an ideal gait feature -- there have been many good geometric features designed -- but to smartly process the data there are at our disposal. This work proposes a gait recognition method without design of novel gait features; instead, we suggest an effective and highly efficient way of processing known types of features. Our method extracts a couple of joint angles from two signature poses within a gait cycle to form a gait pattern descriptor, and classifies the query subject by the baseline 1-NN classier. Not only are these poses distinctive enough, they also rarely accommodate motion irregularities that would result in confusion of identities. We experimentally demonstrate that our gait recognition method outperforms other relevant methods in terms of recognition rate and computational complexity. Evaluations were performed on an experimental database that precisely simulates street-level video surveillance environment.
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bmt-arxiv.pdf   File version 10. 3. 2017

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  • a concrete person Mgr. Lucie Vařechová, uco 106253
  • a concrete person RNDr. Daniel Jakubík, uco 139797
  • a concrete person Mgr. Jolana Surýnková, uco 220973
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premium-award.pdf  9. 8. 2018

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Address within IS
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Address for the users outside IS
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Uploaded/Created
Thu 9. 8. 2018 00:55

Rights

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  • anyone on the Internet
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Right to administer:
  • a concrete person Mgr. Lucie Vařechová, uco 106253
  • a concrete person RNDr. Daniel Jakubík, uco 139797
  • a concrete person Mgr. Jolana Surýnková, uco 220973
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