Invisible, Passive, Continuous and Multimodal Authentication

  • Karen Renaud
  • Heather Crawford
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8045)


Authentication, as traditionally achieved by means of a shared secret, is effortful and deliberate. Frequent and repeated authentication easily becomes a hurdle, an annoyance and a burden. This state of affairs needs to be addressed, and one of the ways of doing this is by moving towards automating the process as much as possible, and reducing the associated effort — ie. reducing its visibility. A shared secret clearly does not have the flexibility to support this, and we need therefore to consider using biometrics. Biometrics are a well-established authentication method. Physiological biometrics require a biometric reader and explicit action by the user. Furthermore, there are always a minority of users who cannot have a particular biometric measured. For example elderly women often lose their fingerprints, and iris biometrics don’t work for people with particular eye conditions.Behavioural biometrics, however, can be collected without the user having to take deliberate action. Hence there is a strong possibility that these biometrics could deliver the invisible and automatic authentication we are striving towards. One big advantage of these biometrics is that, since there is no reader, it is simple to utilise a number of different biometrics, and to combine these to authenticate the user. If one biometric fails the others can still perform authentication.

Here we propose using patterns such as keystroke dynamics, use patterns, and voice analysis techniques to create a multimodal biometric authentication mechanism. These behavioural biometrics take advantage of tasks that the user already performs thereby reducing the need for explicit authentication by more traditional means. In this way, the user is relieved of the burdens of constantly authenticating to multiple applications and devices.


Shared Secret Biometric System Voice Analysis Keystroke Dynamic Multimodal Biometric System 
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 2014

Authors and Affiliations

  • Karen Renaud
    • 1
  • Heather Crawford
    • 1
  1. 1.School of Computing ScienceUniversity of GlasgowGlasgowUK

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