Human Iris Research Articles

Iris‐Inspired Microparticles with a Two‐Factor Authentication Security Feature for Wet‐Phase Enhanced Anti‐Counterfeiting Strategies

AbstractThis article presents an iris‐mimicking polymeric microparticle with randomly generated silica film cracks to be utilized as a wet‐phase micro security taggant. The microparticles are designed to replicate the capillary patterns in the human iris, providing high data capacity and stability, making them ideal for authentication. Furthermore, the microparticles integrate a QR code within the pupillary zone of the iris, enabling pupillary authentication to enhance two‐factor identification and elevate overall security levels an unprecedented feature absent in conventional iris recognition systems. The resulting artificial iris‐mimicking microparticles have high coding efficiency and unique characteristics and can be authenticated in the wet phase, making them suitable for use as micro security taggants.

Place-making Beyond Humans: Environmental Photography with Dots

Thinking with the environmental photography collection on her Instagram account, the author of this visual essay focuses on a conceptual exploration of dots and their agential capacity. From COVID-19-shot blood drops, egg cells, and black fly bites to human irises as devices unlocking phones–all images are unique re-presentations of a posthuman, non-anthropocentric fabric, constantly shaping and reshaping territory. Dots prompt the question: Who contributes to this place besides humans? The author concludes with a discussion on how, in times of climate change, the conceptual insights of the blog can be applied in environmental art education through her place-responsive practice.

42‐2: Self‐Scalable UV Blocking Artificial Iris Operated by Radially Controlled Crosslinking Density with Fast Switching Dynamics

In the anatomy of human eye, the role of iris is crucial since it controls the light level entering the eye and enhances the visibility. Here, we propose a soft contact lens type artificial iris actuated by eternal UV (ultra‐violet) light source, self‐regulating its effective aperture size by varying the crosslinking density of bio‐compatible polymer matrix doped with photochromic dye. For its self‐regulating characteristic, we cured HEMA‐based polymer matrix gradually from center to edge. Since the polymer matrix is polymerized by heat, we utilized NIR (near infra‐red) lamp and gradient grayscale patterned photomask for its gradual characteristic. Thereby, the crosslinking density of central position is higher compared to that of peripheral, causing slow switching dynamics. The gradient difference in switching dynamics leads to artificial iris gaining self‐regulation dynamics of effective aperture. The manufactured artificial iris shows advantage in blocking UV light, self‐regulating, and fast switching dynamics. The switching dynamics of artificial iris under bright and dim light adaptation are less than 50 seconds and 120 seconds, showing similar property with human iris.

Person Authentication System Using Multimodal Biometrics

Biometrics is the automated process used to recognize human by measuring their behavioural and physiological characteristics. Biometrics are generally used either for verification The use of biometric for identification purposes requires that a particular biometric factor be unique for each individual that it can be calculated, and that it is invariant over time. Biometrics such as signatures, photographs, fingerprints, voiceprints and retinal blood vessel patterns all have noteworthy drawbacks. Although signatures and photographs are cheap and easy to obtain and store, they are impossible to identify automatically with assurance, and are easily forget. Human iris on the other hand as an internal organ of the eye and as well protected from the external environment, yet it is easily visible from within one meter of distance makes it a perfect biometric for an identification system with the ease of speed, reliability and automation Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on images of the irises of an individual’s eyes, whose complex random patterns are unique. Proposed system provides a comprehensive implementation of periocular biometrics and a deep insight of various aspects such as utility of peri-ocular region In this project face and eye points are captured using Grassmann algorithm and Gabor filter for eye features extraction. Each trait is analysed separately and given its own score. The results are combined using deep leaning algorithm to provide a single decision in real time environments.

Synthetic Iris Images: A Comparative Analysis between Cartesian and Polar Representation.

In recent years, the advancement of generative techniques, particularly generative adversarial networks (GANs), has opened new possibilities for generating synthetic biometric data from different modalities, including-among others-images of irises, fingerprints, or faces in different representations. This study presents the process of generating synthetic images of human irises, using the recent StyleGAN3 model. The novelty presented in this work consists in producing generated content in both Cartesian and polar coordinate representations, typically used in iris recognition pipelines, such as the foundational work proposed by John Daugman, but hitherto not used in generative AI experiments. The main objective of this study was to conduct a qualitative analysis of the synthetic samples and evaluate the iris texture density and suitability for meaningful feature extraction. During this study, a total of 1327 unique irises were generated, and experimental results carried out using the well-known OSIRIS open-source iris recognition software and the equivalent software, wordlcoin-openiris, newly published at the end of 2023 to prove that (1) no "identity leak" from the training set was observed, and (2) the generated irises had enough unique textural information to be successfully differentiated between both themselves and between them and real, authentic iris samples. The results of our research demonstrate the promising potential of synthetic iris data generation as a valuable tool for augmenting training datasets and improving the overall performance of iris recognition systems. By exploring the synthetic data in both Cartesian and polar representations, we aim to understand the benefits and limitations of each approach and their implications for biometric applications. The findings suggest that synthetic iris data can significantly contribute to the advancement of iris recognition technology, enhancing its accuracy and robustness in real-world scenarios by greatly augmenting the possibilities to gather large and diversified training datasets.

A review of human iris identification and recognition using soft computing techniques

Computer vision and iris recognition are fields that are constantly evolving. During the past ten years, there has been a significant increase in the level of interest in computer vision, Iris recognition, soft computing techniques, neural networks, etc. This paper reviews different research papers on iris recognition. Lastly, it focuses on the international status of iris recognition.

Mitigation of human iris angiogenesis through uPAR/LRP-1 interaction antagonism in an organotypic exvivo model.

Rubeosis Iridis (RI) is characterized by an increase in neovascularization and inflammation factors in the iris. During angiogenesis, the urokinase plasminogen activator (uPA) and its receptor (uPAR) play a pivotal role in extracellular matrix remodeling, where uPAR regulates endothelial cell migration and proliferation through assembly with transmembrane receptors. Here, in the context of hypoxia-induced angiogenesis, the uPA/uPAR system blockage was investigated by using UPARANT in a novel exvivo human iris organotypic angiogenesis assay. The effects of uPA/uPAR system antagonism in the humanized model of ocular pathologic angiogenesis were analyzed by sprouting angiogenesis and protein assays (western, dot blots, and co-immunoprecipitation) and correlated to vascular endothelial growth factor (VEGF) inhibition. Phosphoprotein and co-immunoprecipitation assay illustrated an unidentified antagonism of UPARANT in the interaction of uPAR with the low-density lipoprotein receptor-related protein-1 (LRP-1), resulting in inhibition of β-catenin-mediated angiogenesis in this model. The effects of uPA/uPAR system inhibition were focal to endothelial cells exvivo. Comparison between human iris endothelial cells and human retinal endothelial revealed an endothelial-specific mechanism of β-catenin-mediated angiogenesis inhibited by uPA/uPAR system blockage and not by VEGF inhibition. Collectively, these findings broaden the understanding of the effects of the uPA/uPAR system antagonism in the context of angiogenesis, revealing non-canonical β-catenin downstream effects mediated by LRP-1/uPAR interaction.

Iris-Based Biometric Identification Using a Combination of the Right - Left Iris Statistical Features

A combination between the information extracted for both right iris and left iris could increase the efficacy of the biometric recognition systems. In this paper, we propose a biometric identification method based on density of image patterns extracted from human iris images and the combination and comparison of the right iris and the left iris characteristics. The density of the patters approach for processed images can be a new biometric feature used to implement a biometric recognition system with high performance when a small feature dimension is used. In this way, we can maximize the retention of the effective biometric information. The experiments were conducted on the MMU Iris Database containing 225 images of the left eye and 225 images of the right eye. Two morphological Top-hat and Hit or Miss transforms were implemented to find out the particular pattern of pixels. They allow for the enhancement of detail in images. Then, a statistical feature extraction technique is employed to derive the density of the patterns in morphological transformed images. To assess the density of the patterns differences between the right and left iris data groups, the Pearson’s correlation coefficient (PCC) is computed. We reported very good results with a PCC of 0.6164 (strong and positive correlation) for Top-hat morphological operation whilst the Hit or Miss transform returns a PCC of 0.0127 so there is no relationship between the density of the patterns in the right and left irises.

A review of quality assurance texture for highly pigmented iris recognition using an optimal wavelength band

The iris identification technology that is commercially available often uses segmentation to capture images of the eye within the 850-nm electromagnetic radiation spectrum. In this paper, the strongly pigmented iris image is taken with a camera at 12 different wavelengths, ranging from 420 to 940 nm. The goal is to identify the best wavelength band to expand the iris image wavelengths from 420 to 940 nm in NIR (near infrared) for the purpose of densely pigmented iris recognition. A system that obtains data, usually by digitizing analog channels and storing the data in digital form, is primarily anticipated for imaging the iris at narrow spectral bands in the range of 420–940 nm. This system takes pictures within particular wavelength ranges across the electromagnetic spectrum. Then, 200 human black iris that match the left and right eyes of 100 distinct people are obtained for the assessment. The most common wavelength for recognizing an iris with significant pigmentation is based on texture quality assurance, Equivalent Error Rate (EER), and False Rejection Rate (FRR) matching performance. The visual perception of heightened, detailed local iris texture information supports this effect.

Exploring Phenotypic Diversity of Human Iris Features and Skin Color in Punjabi and Khyber Pakhtunkhwa Population of Pakistan

Exploring Phenotypic Diversity of Human Iris Features and Skin Color in Punjabi and Khyber Pakhtunkhwa Population of Pakistan

Light-adaptable artificial iris with dynamically scalable pupil-aperture function by radially patterned photochromic transition control

The iris is an ocular organ that actively controls the size of the pupil-aperture in response to external light, thereby regulating the amount of light reaching the retina for better visual acquisition. Herein, we propose a light-adaptive pupil-scalable artificial iris for addressing human iris defects with biomimetic self-regulating light control similar to human iris actuation, which is realized by a radially gradient and reversible photoswitching of photochromic dyes doped within a biocompatible hydrogel matrix. The radial photochromic switching of light transmissions was achieved by the gradient patterning of the crosslinking density of the hydrogel matrix using a near-infrared light-absorbing photomask that generated radially thermal gradience during hydrogel matrix polymerization. With the effective pupil-aperture control, the proposed artificial iris exhibited a variation in the visible-light transmittance from ∼82 % at the ultraviolet light (UV) intensity of 0.5 mW/cm2 to ∼43 % at 3.0 mW/cm2 representing the transparent and colored states, respectively. The switching times for the transitions to the colored and transparent states were 27.42 and 112.25 s, respectively, at a UV intensity of 3.0 mW/cm2, which can be faster under the hydrated state. The artificial iris demonstrated potential in biomedical applications by offering reliable light-adaptive attenuation control through human-like pupil-aperture adjustments.

Retina image assessment for microstructural difference detection

The dissimilarities in appearance of the iris texture patters can improve the individual’s recognition and can strength the capabilities of multibiometrics systems. There is an important within-person variation or intraclass variation that produces different patters such as the geometry or iris pigmentation details that change over time or could be different when the right iris and left iris are analyzed. Moreover, the sensing environment such as ambient lighting variations, eye rotation due to the head tilt or inconsistent iris size due to the distance from the camera can also introduce within-person variation. In this paper, we apply the fuzzy edges detection, in a comparative fashion between right iris and left iris, to find the interoperability capability of a particular biometric trait determined primarily by genotype. In other words, we investigated the degree of variation of the irises of the same person. The structural similarity index measure (SSIM) is implemented to investigate the structural similarity between two iris codes. Prior to the similarity analysis, the segmented iris (i.e., annular area between pupil and sclera) is the edges detection based on the fuzzy edge is performed. This operation allows comparisons between the right iris and left iris without any influence of the stretch or dilation of the pupil induced by different illumination conditions. Also, we can estimate if the left and right irises belong to the same or to different individuals. The proposed approach is tested on the MMU1 Iris Database (with 225 images of the left eye and 225 images of the right eye). An average SSIM value of 0.9216, indicates that proposed iris biometrics model effectively differentiates between left and right eyes of the same person. Also, this result indicates that there is recognizable similarity between left and right irises. These results could be useful for certain applications devoted to detect anomalies in the human irises that could be associated to various diseases.

Analysis of genetically determined gene expression suggests role of inflammatory processes in exfoliation syndrome

BackgroundExfoliation syndrome (XFS) is an age-related systemic disorder characterized by excessive production and progressive accumulation of abnormal extracellular material, with pathognomonic ocular manifestations. It is the most common cause of secondary glaucoma, resulting in widespread global blindness. The largest global meta-analysis of XFS in 123,457 multi-ethnic individuals from 24 countries identified seven loci with the strongest association signal in chr15q22–25 region near LOXL1. Expression analysis have so far correlated coding and a few non-coding variants in the region with LOXL1 expression levels, but functional effects of these variants is unclear. We hypothesize that analysis of the contribution of the genetically determined component of gene expression to XFS risk can provide a powerful method to elucidate potential roles of additional genes and clarify biology that underlie XFS.ResultsTranscriptomic Wide Association Studies (TWAS) using PrediXcan models trained in 48 GTEx tissues leveraging on results from the multi-ethnic and European ancestry GWAS were performed. To eliminate the possibility of false-positive results due to Linkage Disequilibrium (LD) contamination, we i) performed PrediXcan analysis in reduced models removing variants in LD with LOXL1 missense variants associated with XFS, and variants in LOXL1 models in both multiethnic and European ancestry individuals, ii) conducted conditional analysis of the significant signals in European ancestry individuals, and iii) filtered signals based on correlated gene expression, LD and shared eQTLs, iv) conducted expression validation analysis in human iris tissues. We observed twenty-eight genes in chr15q22–25 region that showed statistically significant associations, which were whittled down to ten genes after statistical validations. In experimental analysis, mRNA transcript levels for ARID3B, CD276, LOXL1, NEO1, SCAMP2, and UBL7 were significantly decreased in iris tissues from XFS patients compared to control samples. TWAS genes for XFS were significantly enriched for genes associated with inflammatory conditions. We also observed a higher incidence of XFS comorbidity with inflammatory and connective tissue diseases.ConclusionOur results implicate a role for connective tissues and inflammation pathways in the etiology of XFS. Targeting the inflammatory pathway may be a potential therapeutic option to reduce progression in XFS.

Contextual Measures for Iris Recognition

The iris patterns of the human contain a large amount of randomly distributed and irregularly shaped microstructures. These microstructures make the human iris informative biometric traits. To learn identity representation from them, this paper regards each iris region as a potential microstructure and proposes contextual measures (CM) to model the correlations between them. CM adopts two parallel branches to learn global and local contexts in iris image. The first one is the globally contextual measure branch. It measures the global context involving the relationships between all regions for feature aggregation and is robust to local occlusions. Besides, we improve its spatial perception considering the positional randomness of the microstructures. The other one is the locally contextual measure branch. This branch considers the role of local details in the phenotypic distinctiveness of iris patterns and learns a series of relationship atoms to capture contextual information from a local perspective. In addition, we develop the perturbation bottleneck to make sure that the two branches learn divergent contexts. It introduces perturbation to limit the information flow from input images to identity features, forcing CM to learn discriminative contextual information for iris recognition. Experimental results suggest that global and local contexts are two different clues critical for accurate iris recognition. The superior performance on four benchmark iris datasets demonstrates the effectiveness of the proposed approach in within-database and cross-database scenarios.

Improvement of iris recognition technology for biometric identification of a person

This topic is very relevant in the field of artificial intelligence as a direction of pattern recognition. In this work, the iris of the eye is considered as an image. Artificial intelligence makes this technology more accessible for use in CCTV cameras, smartphones and various areas of human activity. The article reflects the results of a study of methods and technologies of pattern recognition on the example of the human iris. The aim of the work was to study methods and technologies for human iris recognition and iris recognition of employees of a particular organization using EyeLock equipment by comparing segmentation results with Daugman standard segmentation. Comparison analysis of segmentation results with standard segmentation can be done by directly measuring the number of correctly segmented irises in both methods, or by indirectly measuring the effect of segmentation on iris recognition performance. The method using the Daugman integral-differential operator has the greatest efficiency. The performance of the neural network has been improved. To use a neural network to classify iris profiles, we selected sets of images (images per person) as training images, and the rest of the images were used as test images. Training time (in seconds): for the Daugman method 170.7, and for the parabolic method 204.7. The Daugman integro-differential operator is applied to the captured image to obtain the "maximum integral derivative of the contour" with ever-increasing radius on "successively decreasing scales" in three parameters: center coordinates and radius. Finding the maximum when the search coordinates deviate along an unwinding spiral. Methods and techniques for pattern recognition have been investigated using the human iris

AFHIRIS: African Human Iris Dataset (Version 1)

Biometric systems remain the most widely used methods for identification and authentication purposes. Their wide acceptability has opened up more research into new application areas of biometric systems. However, biometric research requires an appropriate biometric dataset to validate the proposed technique. This dataset could be privately owned or publicly available for research purposes. In the field of iris biometric research, the iris dataset produced by the Chinese Academy of Sciences (CASIA) is the first, most popular, and widely used publicly available iris dataset. However, the increasing popularity and acceptability of human iris-related research have called for additional benchmarks, and therefore, new publicly available databases of human iris images. Existing publicly available human iris datasets have been collected from non-African subjects; therefore, this dataset is the first publicly available human iris dataset of African descent. Three categories of images were collected from 1028 volunteers that participated in the data collection task. The first category was made up of four iris images that were captured when the volunteers used spectacles, while the second category includes four iris images that were captured when the volunteers wore no spectacles. However, the third category of iris images was obtained from eight volunteers that used print-patterned contact lenses. Only four images were captured from volunteers in this category as they were not asked to put on spectacles. In addition to the iris images captured, soft biometric features such as age, gender, state of origin, weight, and height of the volunteers were also captured. It is strongly believed that this unique collection of iris datasets of African descent will open up new research in the study of the human iris.

Biometric identification of Black Bengal goat: unique iris pattern matching system vs deep learning approach.

Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high-quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system. Forty-nine healthy, disease free, 3 months±6 days old female Black Bengal goats were randomly selected at the farmer's field. Eye images were captured from the left eye of an individual goat at 3, 6, 9, and 12 months of age using a specialized camera made for human iris scanning. iGoat software was used for matching the same individual goats at 3, 6, 9, and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features. The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9, and 12 months of ages were recorded as 81.63%, 90.24%, 44.44%, and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17%, and 87.76% for identification of goat at 3, 6, 9, and 12 months of ages, respectively after training the model. This study strongly supported that deep learning method using eye images could be used as a signature for biometric identification of an individual goat.

Role of Monocytes/Macrophages in the Etiology of Bullous Keratopathy After Argon Laser Iridotomy.

PurposeThe etiologic mechanisms of bullous keratopathy (BK) after argon laser iridotomy (ALI) are still unknown. Therefore, we investigated potential mechanisms on BK after ALI.MethodsCorneal endothelial surface obtained in penetrating keratoplasty for BK after ALI was observed and analyzed immunohistochemically. We investigated how various leukocytes react to cultured human corneal endothelial cells in an inflamed condition and monocytes/macrophages respond to the iris treated by an argon and YAG laser or pigmented and nonpigmented iris treated by an argon laser.ResultsWe detected infiltration of CD68- and CD11b-positive monocytes/macrophages in the posterior surface of trephined corneas obtained during penetrating keratoplasty for BK after ALI in three of the seven eyes with ALI. In vitro, monocytes/macrophages, but not T cells, B cells, neutrophils, or pan-leukocytes, removed many cultured human corneal endothelial cells in the medium stimulated with proinflammatory cytokines. Human pigmented iris tissues treated by the argon laser, but not those treated by the YAG laser, attracted many monocytes/macrophages and formed large, round colonies. Human monocytes/macrophages formed large colonies on the argon laser–treated pigmented iris from C3H mice but not nonpigmented iris from albino BALB/c mice.ConclusionsOur results suggest that monocytes/macrophages, argon laser, and pigmented iris are all involved in the pathogenesis of BK after LI.Translational RelevanceEtiology in BK after ALI has not been clear, but our findings based on clinical and experimental findings give a critical clue to explain possible mechanisms on BK after ALI.

Towards more accurate and efficient human iris recognition model using deep learning technology

In this study, an end-to-end human iris recognition system is presented to automatically identify individuals for high level of security purposes. The deep learning technology based new 2D convolutional neural network (CNN) model is introduced for extracting the features and classifying the iris patterns. Firstly, the iris dataset is collected, preprocessed and augmented. The dataset are expanded and enhanced using data augmentation, histogram equalization (HE) and contrast-limited adaptive histogram equalization (CLAHE) techniques. Secondly, the features of the iris patterns were extracted and classified using CNN. The structure of CNN comprises of convolutional layers and ReLu layers for extracting the features, pooling layers for reducing the parameters, fully connected layer and Softmax layer for classifying the extracted features into N classes. For the training process and updating the weights, the backpropagation algorithm and adaptive moment estimation Adam optimizer are used. The experimental results carried out based on a graphics processing unit (GPU) and using Matlab. The overall training accuracy of the introduced system was 95.33% with a consumption time of 17.59 minutes for training set. While the testing accuracy 100% with a consumption time of 12 seconds. The introduced iris recognition system has been successfully applied.

Formation of three-dimensional cell aggregates expressing lens-specific proteins in various cultures of human iris-derived tissue cells and iPS cells.

Induced pluripotent stem (iPS) cells are widely used as a research tool in regenerative medicine and embryology. In studies related to lens regeneration in the eye, iPS cells have been reported to differentiate into lens epithelial cells (LECs); however, to the best of our knowledge, no study to date has described their formation of three-dimensional cell aggregates. Notably, in vivo studies in newts have revealed that iris cells in the eye can dedifferentiate into LECs and regenerate a new lens. Thus, as basic research on lens regeneration, the present study investigated the differentiation of human iris tissue-derived cells and human iris tissue-derived iPS cells into LECs and their formation of three-dimensional cell aggregates using a combination of two-dimensional culture, static suspension culture and rotational suspension culture. The results revealed that three-dimensional cell aggregates were formed and differentiated into LECs expressing αA-crystallin, a specific marker protein for LECs, suggesting that the cell-cell interaction facilitated by cell aggregation may have a critical role in enabling highly efficient differentiation of LECs. However, the present study was unable to achieve transparency in the cell aggregates; therefore, we aim to continue to investigate the degradation of organelles and other materials necessary to make the interior of the formed cell aggregates transparent. Furthermore, we aim to expand on our current work to study the regeneration of the lens and ciliary body as a whole in vitro, with the aim of being able to restore focusing function after cataract surgery.