TY - GEN
T1 - On the accuracy of eye gaze-driven classifiers for predicting image content familiarity in graphical passwords
AU - Constantinides, Argyris
AU - Belk, Marios
AU - Fidas, Christos
AU - Pitsillides, Andreas
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/6/7
Y1 - 2019/6/7
N2 - Graphical passwords leverage the picture superiority effect to enhance memorability, and reflect today's haptic users' interaction realms. Images related to users' past sociocultural experiences (e.g., retrospective) enable the creation of memorable and secure passwords, while randomly system-assigned images (e.g., generic) lead to easy-to-predict hotspot regions within graphical password schemes. What remains rather unexplored is whether the image type could be inferred during the password creation. In this work, we present a between-subjects user study in which 37 participants completed a recall-based graphical password creation task with retrospective and generic images, while we were capturing their visual behavior. We found that the image type can be inferred within a few seconds in real-time. User adaptive mechanisms might benefit from our work's findings, by providing users early feedback whether they are moving towards the creation of a weak graphical password.
AB - Graphical passwords leverage the picture superiority effect to enhance memorability, and reflect today's haptic users' interaction realms. Images related to users' past sociocultural experiences (e.g., retrospective) enable the creation of memorable and secure passwords, while randomly system-assigned images (e.g., generic) lead to easy-to-predict hotspot regions within graphical password schemes. What remains rather unexplored is whether the image type could be inferred during the password creation. In this work, we present a between-subjects user study in which 37 participants completed a recall-based graphical password creation task with retrospective and generic images, while we were capturing their visual behavior. We found that the image type can be inferred within a few seconds in real-time. User adaptive mechanisms might benefit from our work's findings, by providing users early feedback whether they are moving towards the creation of a weak graphical password.
KW - Graphical passwords
KW - Sociocultural experiences
KW - Visual behavior
UR - http://www.scopus.com/inward/record.url?scp=85068068842&partnerID=8YFLogxK
U2 - 10.1145/3320435.3320474
DO - 10.1145/3320435.3320474
M3 - Conference contribution
AN - SCOPUS:85068068842
T3 - ACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization
SP - 201
EP - 205
BT - ACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization
PB - Association for Computing Machinery, Inc
T2 - 27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019
Y2 - 9 June 2019 through 12 June 2019
ER -