Using reflexive eye movements for fast challenge-response authentication

79Citations
Citations of this article
104Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Eye tracking devices have recently become increasingly popular as an interface between people and consumer-grade electronic devices. Due to the fact that human eyes are fast, responsive, and carry information unique to an individual, analyzing person's gaze is particularly attractive for effortless biometric authentication. Unfortunately, previous proposals for gaze-based authentication systems either suffer from high error rates, or require long authentication times. We build upon the fact that some eye movements can be reflexively and predictably triggered, and develop an interactive visual stimulus for elicitation of reflexive eye movements that supports the extraction of reliable biometric features in a matter of seconds, without requiring any memorization or cognitive effort on the part of the user. As an important benefit, our stimulus can be made unique for every authentication attempt and thus incorporated in a challenge-response biometric authentication system. This allows us to prevent replay attacks, which are possibly the most applicable attack vectors against biometric authentication. Using a gaze tracking device, we build a prototype of our system and perform a series of systematic user experiments with 30 participants from the general public. We investigate the performance and security guarantees under several different attack scenarios and show that our system surpasses existing gaze-based authentication methods both in achieved equal error rates (6.3%) and significantly lower authentication times (5 seconds).

Cite

CITATION STYLE

APA

Sluganovic, I., Roeschlin, M., Rasmussen, K. B., & Martinovic, I. (2016). Using reflexive eye movements for fast challenge-response authentication. In Proceedings of the ACM Conference on Computer and Communications Security (Vol. 24-28-October-2016, pp. 1056–1067). Association for Computing Machinery. https://doi.org/10.1145/2976749.2978311

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free