Towards Reducing the Error Rates in Template Protection for Iris Recognition Using Custom Cuckoo Filters

The need to protect biometric data within iris systems has resulted in a number of template protection schemes. A primary issue with current template protection schemes for iris recognition is the unavoidable biometric error rates, i.e., for any given False Non-Match Rate (FNMR) there is a high False Match Rate (FMR), especially at lower values of FNMR. In this work, we primarily focus on addressing this problem using a new approach with Cuckoo Filtering simultaneously using both stable bits and discriminative bits to derive a stronger template protection scheme. The proposed template protection scheme performs in a robust manner for various configurations as compared to earlier template protection schemes that need empirical fine-tuning. With the set of experiments on a publicly available iris dataset, we benchmark our results against the state-of-art template protection scheme based on Bloom-Filters. Specifically, we demonstrate the gain in performance and robustness of proposed approach at lower FNMR and invariance of performance to configurations of template protection scheme. With a specific configuration of proposed approach, we achieve Genuine Match Rate (GMR) = 100% at FMR = 0:01% and EER = 0% in the best case and GMR = 98:44% at FMR = 0:01% and EER = 0:33% in the worst case on IITD Iris database.