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Gesture-Based Continuous Authentication for Wearable Devices: The Smart Glasses Use Case

  • Jagmohan ChauhanEmail author
  • Hassan Jameel Asghar
  • Anirban Mahanti
  • Mohamed Ali Kaafar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9696)

Abstract

We study the feasibility of touch gesture behavioural biometrics for implicit authentication of users on smart glasses by proposing a continuous authentication system on Google Glass using two classifiers: SVM with RBF kernel, and a new classifier based on Chebyshev’s concentration inequality. Based on data collected from 30 users, we show that such authentication is feasible both in terms of classification accuracy and computational load on Glass. We achieve a classification accuracy of up to 99 % with only 75 training samples using behavioural biometric data from four different types of touch gestures. To show that our system can be generalized, we test its performance on touch data from smartphones and found the accuracy to be similar to Glass. Finally, our experiments on the permanence of gestures show that the negative impact of changing user behaviour with time on classification accuracy can be best alleviated by periodically replacing older training samples with new randomly chosen samples.

Keywords

Support Vector Machine Classification Accuracy True Positive Rate Support Vector Machine Classifier Target User 
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 Switzerland 2016

Authors and Affiliations

  • Jagmohan Chauhan
    • 1
    • 2
    Email author
  • Hassan Jameel Asghar
    • 2
  • Anirban Mahanti
    • 2
  • Mohamed Ali Kaafar
    • 2
  1. 1.UNSWSydneyAustralia
  2. 2.Data61CSIROSydneyAustralia

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