Iriscode Matching Comparator to Improve Decidability of Human Iris Recognition

Abstract

Eye iris has been widely recognized as one of the strongest biometrics attributed to its high accuracy performance. However, any compromised event of iris data potentially leads to severe security and privacy issues because the human iris is permanently linked to individuals and not revocable. Excising protection schemes protect the iris data with the expense of decreased accuracy performance. This paper introduces a new protection scheme to generate a protected template from iris data that can be safely store in the database for future authentication. Experiment results showed that the proposed scheme enjoys a particular S-curve property required to offer strong system security while ensuring high system usability in terms of low false acceptance and false rejection rate.