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A Comparative Evaluation of Finger-Drawn Graphical Password Verification Methods

Authors: Marcos Martinez-Diaz; C. Martin-Diaz; Javier Galbally; Julian Fiérrez;

A Comparative Evaluation of Finger-Drawn Graphical Password Verification Methods

Abstract

Doodle-based graphical passwords represent a challenging scenario due to their high variability and the tendency to be graphically simple. Despite this, doodle-based authentication using touch screens is a promising lightweight user verification method. Several works have been published in this field, although they report in general experimental verification results over small and private databases. In this paper we analyze the performance of several state-of-the-art systems for doodle verification, using the recently acquired DooDB database, which is publicly available. Several algorithms are tested, from the fields of gesture recognition and doodle and signature verification. A comparative study of their performance is done, and future research directions are pointed out.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Average
Average
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