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GPS Trajectory Biometrics: From Where You Were to How You Move

  • Sami Sieranoja
  • Tomi Kinnunen
  • Pasi Fränti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Abstract

In this paper, we study how well GPS data can be used for biometric identification. Previous work has considered only the location and the entire route trajectory pattern. These can reveal the user identity when he repeats his every day moving patterns but not when traveling to new location where no route history is recorded for him. Instead of the absolute location, we model location-independent micro movements measured by speed and direction changes. The resulting short-term trajectory dynamics are modelled by Gaussian mixture model - universal background model (GMM-UBM) classifier from speed and direction change features. The results show that we can indentify users from OpenstreetMap data with an equal error rate (EER) of 19.6 %. Although this is too modest result for user authentication, it indicates that GPS traces do contain identifying cues, which could potentially be used in forensic applications.

Keywords

Global Position System Discrete Cosine Transform Gaussian Mixture Model Global Position System Data Equal Error 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 International Publishing AG 2016

Authors and Affiliations

  1. 1.School of ComputingUniversity of Eastern FinlandJoensuuFinland

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