There is uncertainty around the effect of aging of children on biometric characteristics impacting applications relying on biometric recognition, particularly as the time between enrollment and query increases. Though there have been studies of such effects for iris recognition in adults, there have been few studies evaluating impact in children. This article presents longitudinal analysis from 209 subjects aged 4 to 11 years at enrollment and six additional sessions over a period of 3 years. The influence of time, dilation and enrollment age on iris recognition have been analyzed and their statistical importance has been evaluated. A minor aging effect is noted which is statistically significant, but practically insignificant and is comparatively less important than other variability factors. Practical biometric applications of iris recognition in children are feasible for a time frame of at least 3 years between samples, for ages 4 to 11 years, even in presence of aging, though we note practical difficulties in enrolling young children with cameras not designed for the purpose. To the best of our knowledge, the database used in this study is the only dataset of longitudinal iris images from children for this age group and time period that is available for research.

Iris is one of the most widely used biometric modalities because of its uniqueness, high matching performance, and inherently secure nature. Iris segmentation is an essential preliminary step for iris-based biometric authentication. The authentication accuracy is directly connected with the iris segmentation accuracy. In the last few years, deep-learning-based iris segmentation methodologies have increasingly been adopted because of their ability to handle challenging segmentation tasks and their advantages over traditional segmentation techniques. However, the biggest challenge to the biometric community is the scarcity of open-source resources for adoption for application and reproducibility. This review provides a comprehensive examination of available open-source iris segmentation resources, including datasets, algorithms, and tools. In the process, we designed three U-Net and U-Net++ architecture-influenced segmentation algorithms as standard benchmarks, trained them on a large composite dataset (>45K samples), and created 1K manually segmented ground truth masks. Overall, eleven state-of-the-art algorithms were benchmarked against five datasets encompassing multiple sensors, environmental conditions, demography, and illumination. This assessment highlights the strengths, limitations, and practical implications of each method and identifies gaps that future studies should address to improve segmentation accuracy and robustness. To foster future research, all resources developed during this work would be made publicly available.

Iris is an established modality in biometric recognition applications including consumer electronics, e-commerce, border security, forensics, and de-duplication of identity at a national scale. In light of the expanding usage of biometric recognition, identity clash (when templates from two different people match) is an imperative factor of consideration for a system’s deployment. This study explores system capacity estimation by empirically estimating the constrained capacity of an end-to-end iris recognition system (NIR systems with Daugman-based feature extraction) operating at an acceptable error rate i.e. the number of subjects a system can resolve before encountering an error. We study the impact of six system parameters on an iris recognition system’s constrained capacity-number of enrolled identities, image quality, template dimension, random feature elimination, filter resolution, and system operating point. In our assessment, we analyzed 13.2 million comparisons from 5158 unique identities for each of 24 different system configurations. This work provides a framework to better understand iris recognition system capacity as a function of biometric system configurations beyond the operating point, for large-scale applications.

Biometric recognition systems are fundamental to modern identity management and access control. However, the security and privacy of these systems are severely compromised when adversaries gain access to stored biometric templates, given the immutable link between individuals and their biometric traits. This study presents a benchmark analysis of the trade-offs between dimensionality and system performance in the context of securing iris biometric templates using Fully Homomorphic Encryption (FHE). We examine the impact of Principal Component Analysis (PCA) as a dimensionality reduction technique to enable encrypted-domain computation while maintaining recognition accuracy and reducing computational overhead. In lieu of introducing a new dimensionality reduction technique, this study rigorously benchmarks the applicability of PCA in balancing computational efficiency and recognition accuracy for biometric systems operating under Fully Homomorphic Encryption (FHE) constraints. We evaluate our method on various iris databases, including CASIA-V1, CASIA-V3, UBATH, and IITD. Our approach achieves a $100\%$ True Acceptance Rate (TAR) on CASIA-V1, CASIA-V3, and UBATH and a $99.38\%$ TAR on the IITD database at a $0.1\%$ False Accept Rate (FAR), with a feature dimensionality of 250. This work advances iris recognition security by combining privacy measures with dimensionality reduction for improved authentication accuracy.

In this paper, we evaluate the uniqueness of a hypothetical iris recognition system that relies upon a nonlinear mapping of iris data into a space of Gaussian codewords with independent components. Given the new data representation, we develop and apply a sphere packing bound for Gaussian codewords and a bound similar to Daugman’s to characterize the maximum iris population as a function of the relative entropy between Gaussian codewords of distinct iris classes. As a potential theoretical approach leading toward the realization of the hypothetical mapping, we work with the auto-regressive model fitted into iris data, after some data manipulation and preprocessing. The distance between a pair of codewords is measured in terms of the relative entropy (log-likelihood ratio statistic is an alternative) between distributions of codewords, which is also interpreted as a measure of iris quality. The new approach to iris uniqueness is illustrated using two toy examples involving two small datasets of iris images. For both datasets, the maximum sustainable population is presented as a function of image quality expressed in terms of relative entropy. Although the auto-regressive model may not be the best model for iris data, it lays the theoretical framework for the development of a high-performance iris recognition system utilizing a nonlinear mapping from the space of iris data to the space of Gaussian codewords with independent components.