Iris Texture Research Articles

Iris Recognition based on Statistically Bound Spatial Domain Zero Crossing and Neural Networks

Purpose The iris pattern is an important biological feature of the human body. The recognition of an individual based on an iris pattern is gaining more popularity due to the uniqueness of the pattern among the people. Iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. Methods This article discusses a highly error-resistant technique to implement a biometric recognition system based on the iris portion of the human eye. All iris recognition algorithms of the current day face a major problem of localization errors and the enormous time involved in this localization process. Spatial domain zero crossing may be the simplest and least complex method for localization. Yet, it has not been used due to its high sensitivity to erroneous edges, as a consequence of which more complex and time-consuming algorithms have taken its place. Appropriate statistical bounds imposed on this process help this method to be the least erroneous and time-consuming. Errors were reduced to 0.022% using this approach on the CASIA v1 & v2 datasets. Time consumption in this stage was the least compared to other algorithms. At the comparison stage, most algorithms use multiple comparisons to account for translation and rotation errors. This is time-consuming and very resource-hungry. Results The current approach discusses a robust method based on a single comparison, which works with a correct recognition of over 99.78% which is clearly demonstrated by tests. Conclusions The technique is to use a neural network trained to recognize special statistical and regional parameters unique to every person’s iris. The algorithm also gives sufficient attention to consider illumination errors, elliptical pupils, excess eyelash errors and bad contrast.

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.

Impact of Occlusion Masks on Gender Classification from Iris Texture

Gender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non-iris elements. When, the goal is to classify the gender using exclusively the iris texture, the presence of gender information in the masks may result in apparently higher accuracy, thereby not reflecting the actual gender information present in the iris. However, no measures have been taken to eliminate this information while preserving as much iris information as possible. We propose a novel method to assess the gender information present in the iris more accurately by eliminating gender information in the masks. This consists of pairing iris with similar masks and different gender, generating a paired mask using the OR operator, and applying this mask to the iris. Additionally, we manually fix iris segmentation errors to study their impact on the gender classification. Our results show that occlusion masks can account for 6.92% of the gender classification accuracy on average. Therefore, works aiming to perform gender classification using the iris texture from normalized iris images should eliminate this correlation.

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.

Iris Recognition Approach for Preserving Privacy in Cloud Computing

Biometric identification systems involve securing biometric traits by encrypting them using an encryption algorithm and storing them in the cloud. In recent decades, iris recognition schemes have been considered one of the most effective biometric models for identifying humans based on iris texture, due to their relevance and distinctiveness. The proposed system focuses on encrypting biometric traits. The user’s iris feature vector is encrypted and stored in the cloud. During the matching process, the user’s iris feature vector is compared with the one stored in the cloud. If it meets the threshold conditions, the user is authenticated. Iris identification in cloud computing involves several steps. First, the iris image is pre-processed to remove noise using the Hough transform. Then, the pixel values are normalized, Gabor filters are applied to extract iris features. The features are then encrypted using the AES 128-bit algorithm. Finally, the features of the test image are matched with the stored features on the cloud to verify authenticity. The process ensures the privacy and security of the iris data in cloud storage by utilizing encryption and efficient image processing techniques. The matching is performed by setting an appropriate threshold for comparison. Overall, the approach offers a significant level of safety, effectiveness, and accuracy.

Iris Recognition in Cross-Spectral Based on Histogram of Oriented Gradient and Linear Discriminant Analysis

The use of biological and behavioural characteristics has significantly increased in recent years. Like fingerprints, faces, iris, and others in numerous crucial applications in security and electronic governance. Where recently, Research has concentrated on illuminating the recognition of iris images obtained in different fields across spectra by sensors that can capture double iris images in various areas (environment/lighting) by infrared Nearby and visible illumination (NIR, VIS) due to features unique in the iris texture that differ for the same individual, between their left and right eyes. But there is challenging to match NIR and VIS photographs due to the spectrum differences between pictures captured by near-infrared and visible light. We suggest utilizing the Histogram of Oriented Gradient (HOG) approach to extract meaningful information from images of the iris and implement Normalization to reduce differences in illumination, also apply the LDA for dimensionality reduction and K-Nearest Neighbor and Support Vector Machine as a classifier. The experimental findings were implemented on the PolyU bi-spectral dataset, and the results show that the suggested approach achieved exceptional matching performance by KNN, SVM on (Right to Left) for each (NIR, VIS) or versa (99.14%,99.04%, 97.44%,96.38%) respectively.

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.

Development of a Genetic Algorithm Based Geodesic Active Contour for Iris Based Ethnicity Prediction System

The developments in science and technology have made it possible to use biometrics in applications where it is required to establish or confirm the identity of individuals. Among all possible biometric characteristics, the use of iris texture for recognition of individuals has been proven to be highly reliable. However, existing iris prediction systems have suffered from inability to handle more constrained acquisition (processing non-ideal iris images), high processing time and inappropriate parameter settings which usually results in inaccurate segmentation and poor classification results. This research therefore developed an improved segmentation and classification algorithms for iris-based ethnicity prediction system featuring the three major tribes in Nigeria. Six hundred (600) iris images from three major tribes in Nigeria (Yoruba, Hausa and Ibo) were locally captured for the database. Genetic Algorithm based Geodesic Active Contour (GAGAC) and standard Geodesic Active Control (GAC) were used for iris segmentation while Standard Support Vector Machine (SVM) and Galactic Swarm Optimisation SVM (GSOSVM) was used for iris classification. GAGAC and GSOSVM were used in the designing of the iris-based ethnicity prediction system at segmentation and classification stage. The developed iris-based ethnicity prediction system gave an improved predictive performance over the conventional one. The developed system can be used in different areas where higher security authentication is required.

From coarse to fine: Two-stage deep residual attention generative adversarial network for repair of iris textures obscured by eyelids and eyelashes

We propose a two-stage deep residual attention generative adversarial network (TSDRA-GAN) for inpainting iris textures obscured by eyelids. This two-stage generation approach ensures that the semantic and texture information of the generated images is preserved. In the second stage of the fine network, a modified residual block (MRB) is used to further extract features and mitigate the performance degradation caused by the deepening of the network, thus following the concept of using a residual structure as a component of the encoder. In addition, for the skip connection part of this phase, we propose a dual-attention computing connection (DACC) to computationally fuse the features of the encoder and decoder in both directions to achieve more effective information fusion for iris inpainting tasks. Under completely fair and equal experimental conditions, it is shown that the method presented in this paper can effectively restore original iris images and improve recognition accuracy.

Efficient private key generation from iris data for privacy and security applications

Private key generation based on biometric data is gaining popularity in several cryptographic applications. Some key generation approaches using fingerprint and face data are known for this purpose. The existing approaches, in general, follow direct features from raw data, which may have an issue of revealing users’ biometric data. Further, iris biometrics being a better source for stable and secure key generation, is yet to be explored. In this work, an iris biometric-based key generation approach is proposed. In the proposed method, first, an ensemble L0 Gradient Minimization and an edge-preserving filter are used to extract iris structure from the iris images. Secondly, the iris texture data is normalized, and the consistent region is selected based on a novel statistical method. Thirdly, feature vectors are generated using the ensemble local space-filling curve descriptors and their variants. Fourth, a discriminant feature vector is generated using hybrid feature selection and neighbourhood component analysis. Finally, an interval-based encoding scheme is used to generate a key from the discriminant feature vector. To substantiate the efficacy of the proposed approach, extensive experiments have been carried out with near-infrared images, visible wavelength images, at-a-distance, and on-move images. Further, distinctiveness, dissimilarity, randomness, and security analyses have been carried out to validate the resilience of keys against different attacks. More significantly, a unique key can be generated dynamically, that is, from an online image. The proposed approach is applicable to different cryptographic applications, such as data storage security, remote authentication protocol, blockchain system security, etc.

A novel transconjunctival, intrascleral, ab-externo, knotless, and double-flanged technique with 6-0 Prolene for iridodialysis repair

Small traumatic iridodialysis (ID) may be asymptomatic, but large ones usually cause polycoria and corectopia, leading to symptoms like diplopia, glare, and photophobia. The management of ID, including medical and surgical methods, depends upon the patient’s symptoms. Mild glare and diplopia can be treated either with atropine, antiglaucoma medications, tinted spectacles, colored contact lens, or corneal tattooing, but extensive IDs require surgical options. The surgical techniques are challenging due to the iris texture and the damage encountered during the primary surgery, the narrow anatomical workspace for repair, and the associated surgical complications. Numerous techniques have been described by several authors in the literature; each has its advantages and disadvantages. All the procedures described previously involve conjunctival peritomy, scleral incisions, and suture knots and are time consuming. Here, we report a novel transconjunctival, intrascleral, knotless, and ab-externo, double-flanged technique for repair of large ID with a 1-year follow-up.

Complex-Valued Iris Recognition Network.

In this work, we design a fully complex-valued neural network for the task of iris recognition. Unlike the problem of general object recognition, where real-valued neural networks can be used to extract pertinent features, iris recognition depends on the extraction of both phase and magnitude information from the input iris texture in order to better represent its biometric content. This necessitates the extraction and processing of phase information that cannot be effectively handled by a real-valued neural network. In this regard, we design a fully complex-valued neural network that can better capture the multi-scale, multi-resolution, and multi-orientation phase and amplitude features of the iris texture. We show a strong correspondence of the proposed complex-valued iris recognition network with Gabor wavelets that are used to generate the classical IrisCode; however, the proposed method enables a new capability of automatic complex-valued feature learning that is tailored for iris recognition. We conduct experiments on three benchmark datasets - ND-CrossSensor-2013, CASIA-Iris-Thousand and UBIRIS.v2 - and show the benefit of the proposed network for the task of iris recognition. We exploit visualization schemes to convey how the complex-valued network, when compared to standard real-valued networks, extracts fundamentally different features from the iris texture.

Unconstrained Gender Recognition from Periocular Region Using Multiscale Deep Features

The gender recognition problem has attracted the attention of the computer vision community due to its importance in many applications (e.g., surveillance and human–computer interaction [HCI]). Images of varying levels of illumination, occlusion, and other factors are captured in uncontrolled environments. Iris and facial recognition technology cannot be used on these images because iris texture is unclear in these instances, and faces may be covered by a scarf, hijab, or mask due to the COVID-19 pandemic. The periocular region is a reliable source of information because it features rich discriminative biometric features. However, most existing gender classification approaches have been designed based on hand-engineered features or validated in controlled environments. Motivated by the superior performance of deep learning, we proposed a new method, PeriGender, inspired by the design principles of the ResNet and DenseNet models, that can classify gender using features from the periocular region. The proposed system utilizes a dense concept in a residual model. Through skip connections, it reuses features on different scales to strengthen discriminative features. Evaluations of the proposed system on challenging datasets indicated that it outperformed state-of-the-art methods. It achieved 87.37%, 94.90%, 94.14%, 99.14%, and 95.17% accuracy on the GROUPS, UFPR-Periocular, Ethnic-Ocular, IMP, and UBIPr datasets, respectively, in the open-world (OW) protocol. It further achieved 97.57% and 93.20% accuracy for adult periocular images from the GROUPS dataset in the closed-world (CW) and OW protocols, respectively. The results showed that the middle region between the eyes plays a crucial role in the recognition of masculine features, and feminine features can be identified through the eyebrow, upper eyelids, and corners of the eyes. Furthermore, using a whole region without cropping enhances PeriGender’s learning capability, improving its understanding of both eyes’ global structure without discontinuity.

Effect of pupil dilation on biometric iris recognition systems for personal authentication.

To study the effect of pupil dilation on a biometric iris recognition (BIR) system for personal authentication and identification. A prospective, non-randomized, single-center cohort study was conducted on patients who reported for a routine eye check-up from November 2017 to November 2019 (2 years). An iris scanning device "IRITECH-MK2120U" was used to initially enroll the undilated eyes. Baseline scans were taken after matching with the enrolled database. All eyes were topically dilated and matched again with the enrolled database. The Hamming distance (a measure of disagreement between two iris codes) and recognition status were recorded from the device output, and eyes were evaluated by slit-lamp ophthalmoscopy with special emphasis on pupil shape, size, and texture. All 321 enrolled eyes matched after topical dilation. The pupil size had a significant effect on Hamming distance with a P value <0.05. There were no false matches. A correct recognition rate of 100% was obtained after dilation. No loss of iris texture or pupil shape was observed after dilation. A BIR system is a reliable method for identification and personal authentication after pupil dilation. Topically dilated pupils are not a cause for non-recognition of iris scans.

A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis

Gender classification is attractive in a range of applications, including surveillance and monitoring, corporate profiling, and human-computer interaction. Individuals' identities may be gleaned from information about their gender, which is a kind of soft biometric. Over the years, several methods for determining a person's gender have been devised. Some of the most well-known ones are based on physical characteristics like face, fingerprint, palmprint, DNA, ears, gait, and iris. On the other hand, facial features account for the vast majority of gender classification methods. Also, the iris is a significant biometric trait, because the iris, according to research, remains basically constant during an individual's life. Besides that, the iris is externally visible and is non-invasive to the user, which is important for practical applications. Furthermore, there are already high-quality methods for segmenting and encoding iris images, and the current methods facilitate selecting and extracting attribute vectors from iris textures. This study discusses several approaches to determining gender. The previous works of literature are briefly reviewed. Additionally, there are a variety of methodologies for different steps of gender classification. This study provides researchers with knowledge and analysis of the existing gender classification approaches. Also, it will assist researchers who are interested in this specific area, as well as highlight the gaps and challenges in the field, and finally provide suggestions and future paths for improvement.

Study on IRIS Recognition

This paper gives a survey of literature associated with the one of the biometric popularity structures - iris reputation device. Biometric authentication has come to be one of the critical safety technologies because of the outstanding houses of biometrics compared to other authentication techniques. In view that maximum of the phenotypes of people are unique, physiological tendencies like fingerprints, iris shade, face styles and geometries considered as safety passwords. Among those, iris receives the maximum attention in authentication due to its far reliability. Even the iris textures so that it will be used in iris recognition aren't similar in the left and proper eyes of the identical individual making iris popularity extra comfortable than famous face reputation. The purpose of this paper is to explore latest developments in iris recognition structures and algorithms behind them.

DeepGabor: A Learning-Based Framework to Augment IrisCodes Permanence

For over three decades, the Gabor-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IrisCode</i> approach has been acknowledged as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gold standard</i> for iris recognition, mainly due to the high entropy and binary nature of its signatures. This method is highly effective in large scale environments (e.g., national ID applications), where millions of comparisons per second are required. However, it is known that non-linear deformations in the iris texture, with fibers vanishing/appearing in response to pupil dilation/contraction, often flip the signature coefficients, being the main cause for the increase of false rejections. This paper addresses this problem, describing a customised Deep Learning (DL) framework that: 1) virtually emulates the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IrisCode</i> feature encoding phase; while also 2) detects the deformations in the iris texture that may lead to bit flipping, and autonomously adapts the filter configurations for such cases. The proposed DL architecture seamlessly integrates the Gabor kernels that extract the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IrisCode</i> and a multi-scale texture analyzer, from where the biometric signatures yield. In this sense, it can be seen as an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive encoder</i> that is fully compatible to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IrisCode</i> approach, while increasing the permanence of the signatures. The experiments were conducted in two well known datasets (CASIA-Iris-Lamp and CASIA-Iris-Thousand) and showed a notorious decrease of the mean/standard deviation values of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">genuines</i> distribution, at expenses of only a marginal deterioration in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">impostors</i> scores. The resulting decision environments consistently reduce the levels of false rejections with respect to the baseline for most operating levels (e.g., over 50% at 1 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> FAR values). The source code of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepGabor</i> encoder is available at: https://github.com/hugomcp/DeepGabor.

Lymphome conjonctival révélant un lymphome systémique: à propos d´un cas

We report the case of a 60-year-old female patient with no specific history, presenting to the emergency ophthalmology with conjunctival mass in left and right eyes that had gradually increased over the past three months. Ophthalmological examination of both eyes showed uncorrected visual acuity 8/10, preserved ocular motility, present and symmetrical pupillary light reflex, bilateral well-vascularized, salmon pink conjunctival swelling filling the upper conjunctival cul-de-sac (A). Examination of the anterior segment showed clear cornea, anterior chamber of good depth and optically empty, normal iris texture and color, grade I nuclear cataract, and filamentous opacity in the vitreous chamber. Fundus examination objectified small optic disc showing clearly visible contour and excavation (3/10), good foveal reflex and normal peripheral retina. Orbital MRI was performed, which showed bilateral palpebral infiltration, predominantly on the upper conjunctival surface with iso-signal on T2 and uniformly enhanced after injection. Lacrimal glands were invaded. Conical fat, eyeball and optic nerve were spared (B). After a multidisciplinary meeting, it was decided to perform conjunctival biopsy with immunohistochemistry, which showed malignant non-Hodgkin B lymphoma of the marginal malt zone CD20 +, CD 10-, CD23- , anti-cyclin D1, with proliferation index 8% (C). The patient was then referred to the hematology department for chemotherapy.

The Two-Stage Recognition Method Based on Texture Signals of the Heterogeneous Unsteady Iris

In this paper, a two-stage multi-category recognition structure based on texture features is proposed. This method can solve the problem of the decline in recognition accuracy in the scene of lightweight training samples. Besides, the problem of recognition effect different in the same recognition structure caused by the unsteady iris can also be solved. In this paper’s structure, digitized values of the edge shape in the iris texture of the image are set as the texture trend feature, while the differences between the gray values of the image obtained by convolution are set as the grayscale difference feature. Furthermore, the texture trend feature is used in the first-stage recognition. The template category that does not match the tested iris is the elimination category, and the remaining categories are uncertain categories. Whereas, in the second-stage recognition, uncertain categories are adopted to determine the iris recognition conclusion through the grayscale difference feature. Then, the experiment results using the JLU iris library show that the method in this paper can be highly efficient in multi-category heterogeneous iris recognition under lightweight training samples and unsteady state.

Non-Cooperative Iris Segmentation: A Two-Stage Approach

Aim: The accuracy of the non-cooperative iris recognition is highly dependent on the proper segmentation of the iris region from the input eye image. The traditional non-cooperative iris segmentation algorithms decrease significantly because of several noise factors such as specular reflections, occlusions, eyelashes, and eyelids. However, several techniques are developed to overcome these drawbacks in the iris segmentation process; it is still a challenging task to localize the iris texture regions. Background: Recently, segmentation of iris image is the most important process in a robust iris recognition system due to the images captured from non-cooperative environments which introduce occlusions, blur, specular reflections, and off-axis. However, several techniques are developed to overcome these drawbacks in the iris segmentation process; it is still a challenging task to localize the iris texture regions. In this research, an effective two-stage of iris segmentation technique is proposed in a non-cooperative environment. Objective: To proposed an effective two-stage of iris segmentation technique in a non-cooperative environment. Methods: Modified Geodesic Active Contour-based level set segmentation with Particle Swarm Optimization (PSO) is employed for iris segmentation. In this, the PSO algorithm is used to minimize the energy of the gradient descent equation in a region-based level set segmentation algorithm. The global threshold-based segmentation (enhanced Otsu’s method) is employed for pupil region segmentation. Results: The experiment considered two well-known databases such as UBIRIS.V1 and UBIRIS.V2. The simulation outcomes demonstrate that the proposed novel approach attained more accurate and robust iris segmentation under non-cooperative conditions. Also, the results of the modified Geodesic Active Contour-based level set segmentation with the PSO algorithm attained better results than the conventional segmentation techniques. Conclusion: An effective two-stage of iris segmentation is proposed in a non-cooperative environment using Geodesic active contour-based level set segmentation with particle swarm optimization and an enhanced Otsu method is employed for pupil segmentation. The proposed two-stage of iris segmentation technique segments the iris in its genuine shape which will provide a more robust and precise iris recognition system.