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1. A label-free optical biosensor-based point-of-care test for the rapid detection of Monkeypox virus

   Aslan, Mete; Seymour, Elif; Brickner, Howard; Ünlü, Selim; Ray, Partha (February 2025, Biosensors bioelectronics)

   Diagnostic approaches that combine the high sensitivity and specificity of laboratory-based digital detection with the ease of use and affordability of point-of-care (POC) technologies could revolutionize disease diagnostics. This is especially true in infectious disease diagnostics, where rapid and accurate pathogen detection is critical to curbing the spread of disease. We have pioneered an innovative label-free digital detection platform that utilizes Interferometric Reflectance Imaging Sensor (IRIS) technology. IRIS leverages light interference from an optically transparent thin film, eliminating the need for complex optical resonances to enhance the signal by harnessing light interference and the power of signal averaging in shot-noise-limited operation In our latest work, we have further improved our previous 'Single-Particle' IRIS (SP-IRIS) technology by allowing the construction of the optical signature of target nanoparticles (whole virus) from a single image. This new platform, 'Pixel-Diversity' IRIS (PD-IRIS), eliminated the need for z-scan acquisition, required in SP-IRIS, a time-consuming and expensive process, and made our technology more applicable to POC settings. Using PD-IRIS, we quantitatively detected the Monkeypox virus (MPXV), the etiological agent for Monkeypox (Mpox) infection. MPXV was captured by anti-A29 monoclonal antibody (mAb 69-126-3) on Protein G spots on the sensor chips and were detected at a limit-of-detection (LOD) - of 200 PFU/mL (∼3.3 aM). PD-IRIS was superior to the laboratory-based ELISA (LOD - 1800 PFU/mL) used as a comparator. The specificity of PD-IRIS in MPXV detection was demonstrated using Herpes simplex virus, type 1 (HSV-1), and Cowpox virus (CPXV). This work establishes the effectiveness of PD-IRIS and opens possibilities for its advancement in clinical diagnostics of Mpox at POC. Moreover, PD-IRIS is a modular technology that can be adapted for the multiplex detection of pathogens for which high-affinity ligands are available that can bind their surface antigens to capture them on the sensor surface. 

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2. 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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3. Triplet loss-based deep learning frameworks for off-angle iris recognition

   https://doi.org/10.1117/12.3055314  

   Tebor, Daniel; Karakaya, Mahmut (May 2025, SPIE)

   Alam, Mohammad S; Asari, Vijayan K (Ed.)

   Iris recognition is a reliable biometric identification method known for its low false-acceptance rates. However, capturing ideal iris images is often challenging and time-consuming, which can degrade the performance of iris recognition systems when using non-ideal images. Enhancing iris recognition performance for non-ideal images would expedite and make the process more flexible. Off-angle iris images are a common type of non-ideal iris images, and converting them to their frontal version is not as simple as making geometric transformations on the off-angle iris images. Due to challenging factors such as corneal refraction and limbus occlusion, frontal projection requires a more comprehensive approach. Pix2Pix generative adversarial networks (GANs), with their pairwise image conversion capability, provide the ideal foil for such a tailored approach. We demonstrate how Pix2Pix GANs can effectively be used for the problem of converting off-angle iris images to frontal iris images. We provide a comprehensive exploration of techniques using Pix2Pix GAN to enhance off-angle to frontal iris image transformation by introducing variations in the loss functions of Pix2Pix GAN for better capturing the iris textures and the low contrast, changing the medium of input from normalized iris to iris codes, and ultimately delving deeper into studying which regions of the Gabor filters contribute the most to iris recognition performance. 

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4. 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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5. 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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