Biometric Based Personal Authentication Using Eye Movement Tracking

The paper provides an insight into the newly emerging field of Eye Movement Tracking (EMT), spanning across various facets of EMT, from acquisition to authentication. The second most cardinal problem of machine learning after overfitting, i.e. Curse of Dimensionality is dealt with using a novel method of error analysis on EMT based personal authentication through a dimensionality reduction algorithm. We apply both static and dynamic methods for the dimensionality reduction in EMT to achieve promising results of personal authentication and compare these results based on speed and accuracy of both the methods. A decision tree classifier is used in two cases (static and dynamic) of EMT for the classification. The novel method presented in this paper is not limited to EMT and it can be emulated for other biometric modalities as well.