• DocumentCode
    2084136
  • Title

    Interframe variation vector-based gait recognition

  • Author

    Su, Song-Zhi ; Wang, Li ; Li, Shao-Zi

  • Author_Institution
    Cognitive Sci. Dept., Xiamen Univ., Xiamen, China
  • Volume
    1
  • fYear
    2008
  • fDate
    17-19 Nov. 2008
  • Firstpage
    707
  • Lastpage
    712
  • Abstract
    The reliable extraction of characteristic gait features from image sequences is an important issue in gait recognition. In this paper we propose a simple, but efficient approach to extract gait feature. In view of the spatio-temporal motion characteristic of gait, we adopt the shape variation information between successive frames to denote gait information, called interframe variation vector- IVV. Different with other features, IVV doesn¿t condense a gait sequence into single image, which ignores the spatial attribute, but records the whole moving process in a IVV sequence. This signature not only preserves all the movements of limbs of the body, above all it maintains the order of movement which is the nature of gait. Compared with other gait signatures, it can fully capture the essential feature of gait. Experimental result on CASIA gait database shows that our proposed method has an encouraging recognition performance.
  • Keywords
    biometrics (access control); feature extraction; gait analysis; image motion analysis; image recognition; image sequences; spatiotemporal phenomena; vectors; biometric analysis; body limb movement; gait feature extraction; human gait recognition; image sequence; interframe variation vector; shape variation information; spatio-temporal gait motion characteristic; Biological system modeling; Biometrics; Cognitive science; Data mining; Humans; Intelligent systems; Knowledge engineering; Reliability engineering; Shape; Spatial databases;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent System and Knowledge Engineering, 2008. ISKE 2008. 3rd International Conference on
  • Conference_Location
    Xiamen
  • Print_ISBN
    978-1-4244-2196-1
  • Electronic_ISBN
    978-1-4244-2197-8
  • Type

    conf

  • DOI
    10.1109/ISKE.2008.4731022
  • Filename
    4731022