TY  - THES
A1  - Akbari, Daniyal
T1  - Multi-model optimization of gaze and touch-based PIN Entry
N2  - Knowledge-based authentication methods are vulnerable to Shoulder surfing phenomenon.
The widespread usage of these methods and not addressing the limitations it has could result in the user’s information to be compromised. User authentication method ought to be effortless to use and efficient, nevertheless secure.
The problem that we face concerning the security of PIN (Personal Identification Number) or password entry is shoulder surfing, in which a direct or indirect malicious observer could identify the user sensitive information. To tackle this issue we present TouchGaze which combines gaze signals and touch capabilities, as an input method for entering user’s credentials. Gaze signals will be primarily used to enhance targeting and touch for selecting. In this work, we have designed three different PIN entry method which they all have similar interfaces. For the evaluation, these methods were compared based on efficiency, accuracy, and usability. The results uncovered that despite the fact that gaze-based methods require extra time for the user to get familiar with yet it is considered more secure. In regards to efficiency, it has the similar error margin to the traditional PIN entry methods.
Y1  - 2018
UR  - https://kola.opus.hbz-nrw.de/frontdoor/index/index/docId/1675
UR  - https://nbn-resolving.org/urn:nbn:de:kola-16758
ER  -