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Gait-Based Person Identification Using Motion Interchange Patterns

  • Gil FreidlinEmail author
  • Noga Levy
  • Lior Wolf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)

Abstract

Understanding human motion in unconstrained 2D videos has been a central theme in Computer Vision research, and over the years many attempts have been made to design effective representations of video content. In this paper, we apply to gait recognition the Motion Interchange Patterns (MIP) framework, a 3D extension of the LBP descriptors to videos that was successfully employed in action recognition. This effective framework encodes motion by capturing local changes in motion directions. Our scheme does not rely on silhouettes commonly used in gait recognition, and benefits from the capability of MIP encoding to model real world videos. We empirically demonstrate the effectiveness of this modeling of human motion on several challenging gait recognition datasets.

Keywords

MIP LBP Gait recognition CASIA TUMGAID 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Tel-Aviv UniversityTel AvivIsrael

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