Open AccessBiometric Authentication Using Mouse and Eye Movement Data
Author(s) -
J. T. Anita Rose,
Yudong Liu,
Ahmed Awad
Publication year - 2017
Publication title -
journal of cyber security and mobility
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.198
H-Index - 9
eISSN - 2245-4578
pISSN - 2245-1439
DOI - 10.13052/2245-1439.611
Subject(s) - computer science , biometrics , salient , artificial neural network , eye movement , artificial intelligence , classifier (uml) , modalities , authentication (law) , data set , machine learning , computer vision , pattern recognition (psychology) , computer security , social science , sociology
Previous biometric systems have attempted to identify users solely by eye or mouse data. In this paper, we seek to find out if combining both kinds of data produces better results. In our system, mouse movement and eye movement data are gathered from each user simultaneously, a set of salient features are proposed, and a Neural Network classifier is trained on this data to uniquely identify users. After going through this process and investigating several Neural Network based classification models we conclude that combining the modalities results in a more accurate authentication decision and will become practical once the hardware is more widespread.