Quantifying Gait Similarity: User Authentication and Real-World Challenge

  • Marc Bächlin
  • Johannes Schumm
  • Daniel Roggen
  • Gerhard Töster
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


Template-based approaches using acceleration signals have been proposed for gait-based biometric authentication. In daily life a number of real-world factors affect the users’ gait and we investigate their effects on authentication performance. We analyze the effect of walking speed, different shoes, extra load, and the natural variation over days on the gait. Therefore we introduce a statistical Measure of Similarity (MOS) suited for template-based pattern recognition. The MOS and actual authentication show that these factors may affect the gait of an individual at a level comparable to the variations between individuals. A change in walking speed of 1km/h for example has the same MOS of 20% as the in-between individuals’ MOS. This limits the applicability of gait-based authentication approaches. We identify how these real-world factors may be compensated and we discuss the opportunities for gait-based context-awareness in wearable computing systems.


Feature Vector User Authentication Dynamic Time Warping Heel Strike Gait Recognition 
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 2009

Authors and Affiliations

  • Marc Bächlin
    • 1
  • Johannes Schumm
    • 1
  • Daniel Roggen
    • 1
  • Gerhard Töster
    • 1
  1. 1.Wearable Computing LaboratoryETH ZürichSwitzerland

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