Authentication using graphical passwords: Effects of tolerance and image choice

Susan Wiedenbeck, Jim Waters, Jean Camille Birget, Alex Brodskiy, Nasir Memon

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Graphical passwords are an alternative to alphanumeric passwords in which users click on images to authenticate themselves rather than type alphanumeric strings. We have developed one such system, called PassPoints, and evaluated it with human users. The results of the evaluation were promising with respect to rmemorability of the graphical password. In this study we expand our human factors testing by studying two issues: the effect of tolerance, or margin of error, in clicking on the password points and the effect of the image used in the password system. In our tolerance study, results show that accurate memory for the password is strongly reduced when using a small tolerance (10 × 10 pixels) around the user's password points. This may occur because users fail to encode the password points in memory in the precise manner that is necessary to remember the password over a lapse of time. In our image study we compared user performance on four everyday images. The results indicate that there were few significant differences in performance of the images. This preliminary result suggests that many images may support memorability in graphical password systems.

Original languageEnglish (US)
Title of host publicationSOUPS 2005 - Proceedings of the Symposium on Usable Privacy and Security
Number of pages12
StatePublished - 2005
EventSymposium on Usable Privacy and Security, SOUPS 2005 - Pittsburgh, PA, United States
Duration: Jul 6 2005Jul 8 2005

Publication series

NameACM International Conference Proceeding Series


OtherSymposium on Usable Privacy and Security, SOUPS 2005
Country/TerritoryUnited States
CityPittsburgh, PA


  • Authentication
  • Graphical passwords
  • Password images
  • Tolerance

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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