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Uniprojective Features for Gait Recognition

  • Daoliang Tan
  • Kaiqi Huang
  • Shiqi Yu
  • Tieniu Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

Recent studies have shown that shape cues should dominate gait recognition. This motivates us to perform gait recognition through shape features in 2D human silhouettes. In this paper, we propose six simple projective features to describe human gait and compare eight kinds of projective features to figure out which projective directions are important to walker recognition. First, we normalize each original human silhouette into a square form. Inspired by the pure horizontal and vertical projections used in the frieze gait patterns, we explore the positive and negative diagonal projections with or without normalizing silhouette projections and obtain six new uniprojective features to characterize walking gait. Then this paper applies principal component analysis (PCA) to reduce the dimension of raw gait features. Finally, we recognize unknown gait sequences using the Mahalanobis-distance-based nearest neighbor rule. Experimental results show that the horizontal and diagonal projections have more discriminative clues for the side-view gait recognition and that the projective normalization generally can improve the robustness of projective features against the noise in human silhouettes.

Keywords

Gait recognition projective features PCA 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Daoliang Tan
    • 1
  • Kaiqi Huang
    • 1
  • Shiqi Yu
    • 1
  • Tieniu Tan
    • 1
  1. 1.Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100080P.R. China

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