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1. A Comprehensive Evaluation of Iris Segmentation on Benchmarking Datasets

   https://doi.org/10.3390/s24217079  

   Sumi, Mst Rumana; Das, Priyanka; Hossain, Afzal; Dey, Soumyabrata; Schuckers, Stephanie (November 2024, Sensors)

   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. 

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2. Iris Recognition for Infants

   Bhuiyan, Rasel Ahmed; Trokielewicz, Mateusz; Maciejewicz, Piotr; Bucher, Sherri; Czajka, Adam (February 2025, IEEE)

   Non-invasive, efficient, physical token-less, accurate and stable identification methods for newborns may prevent baby swapping at birth, limit baby abductions and improve post-natal health monitoring across geographies, within the context of both the formal (i.e., hospitals) and informal (i.e., humanitarian and fragile settings) health sectors. This paper explores the feasibility of application iris recognition to build biometric identifiers for 4-6 week old infants. We (a) collected near infrared (NIR) iris images from 17 infants using a specially-designed NIR iris sensor; (b) evaluated six iris recognition methods to assess readiness of the state-of-the-art iris recognition to be applied to newborns and infants; (c) proposed a new segmentation model that correctly detects iris texture within infants iris images, and coupled it with several iris texture encoding approaches to offer, to the first of our knowledge, a fully-operational infant iris recognition system; and, (d) trained a StyleGAN-based model to synthesize iris images mimicking samples acquired from infants to deliver to the research community privacy-safe in- fant iris images. The proposed system, incorporating the specially-designed iris sensor and segmenter, and applied to the collected infant iris samples, achieved Equal Error Rate (EER) of 3% and Area Under ROC Curve (AUC) of 99%, compared to EER20% and AUC88% obtained for state of the art adult iris recognition systems. This suggests that it may be feasible to design methods that succesfully extract biometric features from infant irises. 

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3. Uniqueness of Iris Pattern Based on the Auto-Regressive Model

   https://doi.org/10.3390/s24092797  

   Schmid, Natalia A; Valenti, Matthew C; Hampel, Katelyn M; Zuo, Jinyu; Das, Priyanka; Schuckers, Stephanie; Skufca, Joseph (May 2024, Sensors)

   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. 

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4. Fragile Bits in Off-angle Iris Recognition

   https://doi.org/10.1109/IWBF50991.2021.9465098  

   Karakaya, Mahmut (May 2021, 2021 IEEE International Workshop on Biometrics and Forensics)

   null (Ed.)

   As an emerging biometric research, standoff iris recognition systems focus on recognition of non-cooperative subjects in much less constrained environments where their captured images are likely to be non-ideal including being off-angle. Iris biometrics convert unwrapped iris textures into binary iris codes to compare them with other saved codes by measuring their Hamming Distances. The similarity calculation assumes an equal contribution of each individual pixel in iris codes. However, previous studies showed that some pixels (aka. fragile bits) are more error prone than others even in frontal iris images. In addition, off-angle iris images are affected by several challenging factors including corneal refraction and limbus occlusion. These challenges in off-angle images also increase the fragility of bits in iris codes. This paper first presents the pixel inconsistency in iris codes of off-angle images using elliptical segmentation and normalization. The pixel fragility is a result of iris codes warping due to the refraction of light in cornea and occlusion of iris texture at limbus. As another contribution, we propose to identify these fragile pixels in iris codes using edge detection and eliminating them in Hamming distance calculation by masking these fragile bits. Based on the results, the proposed method improves the recognition performance in off-angle iris images where the average genuine Hamming distance score reduced from 0.3082 to 0.1244 and the equal error rate is lowered 19%. 

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5. Iris Presentation Attack via Textured Contact Lens in Unconstrained Environment

   https://doi.org/10.1109/WACV.2018.00061  

   Yadav, Daksha; Kohli, Naman; Yadav, Shivangi; Vatsa, Mayank; Singh, Richa; Noore, Afzel (March 2018, IEEE Winter Conference on Applications of Computer Vision (WACV))

   The widespread use of smartphones has spurred the research in mobile iris devices. Due to their convenience, these mobile devices are also utilized in unconstrained outdoor scenarios. This has necessitated the development of reliable iris recognition algorithms for such uncontrolled environment. At the same time, iris presentation attacks pose a major challenge to current iris recognition systems. It has been shown that print attacks and textured contact lens may significantly degrade the iris recognition performance. Motivated by these factors, we present a novel Mobile Uncontrolled Iris Presentation Attack Database (MUIPAD). The database contains more than 10,000 iris images that are acquired with and without textured contact lenses in indoor and outdoor environments using a mobile sensor. We also investigate the efficacy of textured contact lens in identity impersonation and obfuscation. Moreover, we demonstrate the effectiveness of deep learning based features for iris presentation attack detection on the proposed database. 

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