NDBIris with Better Unlinkability.

Iris recognition is one of the mainstream biometric recognition methods. Protecting iris data to prevent personal privacy leakage is significant to the popularity of iris recognition. Negative database is a new type of privacy protection technique. We proposed a promising method (called NDBIris) of iris template protection based on negative databases in previous work. However, its unlinkability is vulnerable under typical parameter settings (e.g. p(1) = 0.8, p(2) = 0.14) and it does not protect the privacy of real-time iris data from users for recognition. This paper proposes an improved version called NDBIris-II to achieve better unlinkability and protect the real-time iris data. Specifically, a noninvertible transform using local sorting is performed before converting iris data into negative databases. Moreover, a method for estimating the similarity between iris data from negative databases is proposed to support effective iris recognition. Finally, an iris template in the form of negative database is generated for each iris data, and it is stored and used during iris recognition instead of raw iris data for privacy protection. Experimental results on iris database CASIA-IrisV3-Interval demonstrate that the proposed method could maintain recognition performance while achieving better unlinkability and protecting real-time iris data.