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Low Resolution Gait Recognition with High Frequency Super Resolution

  • Junping Zhang
  • Yuan Cheng
  • Changyou Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5351)

Abstract

Being non-invasive and effective at a distance, recognition suffers from low resolution sequence case. In this paper, we attempt to address the issue through the proposed high frequency super resolution method. First, a group of high resolution training gait images are degenerated for capturing high-frequency information loss. Then the combination of neighbor embedding with interpolation methods is employed for learning and recovering a high resolution test image from low resolution counterpart. Finally, classification is performed based on nearest neighbor classifier. The experiment indicates that the proposed method can effectively improve the accuracy of gait recognition under low resolution case.

Keywords

Gait Recognition Super Resolution Gait Energy Image 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Junping Zhang
    • 1
    • 2
  • Yuan Cheng
    • 2
  • Changyou Chen
    • 2
  1. 1.Shanghai Key Laboratory of Intelligent Information ProcessingChina
  2. 2.Department of Computer Science and EngineeringFudan universityShanghaiChina

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