Feature Extraction and Selection for Recognizing Humans by Their Gait

  • Jang-Hee Yoo
  • Mark S. Nixon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4292)


We describe an efficient and effective feature extraction and selection method for identifying humans by their gait. A sequential set of 2D stick figures is extracted from gait silhouette data by determining the joint angles and body points, and it is used to represent the gait signature that is primitive data for extracting motion parameters. The motion parameters in the gait signatures are stride length, cycle time, speed, and joint angles, and the gait features are extracted from these motion parameters. By measuring a class separability of the extracted features, important features are selected from original feature sets for classifying human in the gait patterns. Then, a k-NN classifier is used to analyze the discriminatory ability of the selected features. In experiments, higher gait classification performances, which are 96.7%, have been achieved.


Feature Vector Joint Angle Gait Cycle Stride Length Correct Classification Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jang-Hee Yoo
    • 1
  • Mark S. Nixon
    • 2
  1. 1.ETRI-Information Security Research DivisionDaejeonSouth Korea
  2. 2.School of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUnited Kingdom

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