Iris Recognition Accuracy Research Articles

Performance evaluation of efficient segmentation and classification based iris recognition using sheaf attention network

Iris recognition, a precise biometric identification technique, relies on the distinct epigenetic patterns within the iris. Existing methods often face challenges related to segmentation accuracy and classification efficiency. To improve the accuracy and efficiency of iris recognition systems, this research proposes an innovative approach for iris recognition, focusing on efficient segmentation and classification using Convolutional neural networks with Sheaf Attention Networks (CSAN). Main objective is to develop an integrated framework that optimizes iris segmentation and classification. Subsequently, dense extreme inception multipath guided up sampling network is employed for accurate segmentation. Finally, classifiers including convolutional neural network with sheaf attention networks are evaluated. The findings indicate that the proposed method achieves superior iris recognition accuracy and robustness, making it suitable for applications such as secure authentication and access control. By comparing with existing approaches CSAN obtains 99.98%, 99.35%, 99.45% and 99.65% accuracy for the four different proposed datasets respectively.

Instant learning based on deep neural network with linear discriminant analysis features extraction for accurate iris recognition system

Instant learning based on deep neural network with linear discriminant analysis features extraction for accurate iris recognition system

Research on Iris Feature Extraction and Recognition Technology Based on Deep Learning

Abstract Certain biological information or behavioral information of a person can achieve the effect of characterizing an individual, and by combining the computer to extract the corresponding information, identity authentication is achieved. In a variety of biometrics, iris relative to fingerprints, handwriting and face, belongs to the structure within the human eye, if you want to steal is very difficult, in order to improve the safety factor, so the iris is used for authentication to achieve iris recognition. This study is based on deep learning iris recognition matching, in order to be able to effectively improve the accuracy of iris recognition, experiments are carried out. Evaluation metrics are performed through Hamming distance to calculate the correct recognition rate to ensure that the iris information can be accurately represented. This study mainly uses the improved PCHIP-LMD algorithm and CNN algorithm, the LMD algorithm is more context-aware, has better generalization ability, flexibility and scalability, while the CNN algorithm has the advantages of local awareness, parameter sharing and automatic parameter learning. In this study, we compare the correct recognition rate of iris recognition between improved PCHIP-LMD and CNN algorithms and get the conclusion that the correct recognition rate of the improved PCHIP-LMD algorithm is only 78%, which is much smaller than that of the CNN algorithm which is 92%, and we get the conclusion that LMD algorithm is suitable for iris recognition with few samples, and it is more suitable to use CNN algorithm when the sample images are too many. CNN algorithm. With the development of technology, the application of iris recognition will be more and more, I believe that soon will be widely popularized in daily life and work.

Comparative Analysis of Firefly and Modified Firefly Algorithms in Multimodal Biometric Authentication Systems

Abstract: This research focuses on addressing the challenges of high-dimensional feature spaces and selecting significant features in multimodal biometric systems. Feature-level fusion poses persistent issues that require extensive investigation due to its potential for enhancing biometric recognition accuracy. This study proposes the integration of meta-heuristic optimization techniques into the feature selection phase of a multimodal biometric system, prior to the classification phase, to identify the most relevant features from two distinct biological traits. To determine the authorization status of an individual, the fused feature vectors are inputted into a Support Vector Machine (SVM) classifier. The study leverages the physiological biometrics of the face and iris to validate the findings of previous research, highlighting the exceptional accuracy of iris recognition and the natural acceptability of face recognition for identity verification purposes.

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.

Robust Iris-Localization Algorithm in Non-Cooperative Environments Based on the Improved YOLO v4 Model

Iris localization in non-cooperative environments is challenging and essential for accurate iris recognition. Motivated by the traditional iris-localization algorithm and the robustness of the YOLO model, we propose a novel iris-localization algorithm. First, we design a novel iris detector with a modified you only look once v4 (YOLO v4) model. We can approximate the position of the pupil center. Then, we use a modified integro-differential operator to precisely locate the iris inner and outer boundaries. Experiment results show that iris-detection accuracy can reach 99.83% with this modified YOLO v4 model, which is higher than that of a traditional YOLO v4 model. The accuracy in locating the inner and outer boundary of the iris without glasses can reach 97.72% at a short distance and 98.32% at a long distance. The locating accuracy with glasses can obtained at 93.91% and 84%, respectively. It is much higher than the traditional Daugman's algorithm. Extensive experiments conducted on multiple datasets demonstrate the effectiveness and robustness of our method for iris localization in non-cooperative environments.

Research on strabismus iris segmentation model based on deep snake multitask learning

Biometrics is widely used in various fields as a means of biological individual identification, and iris recognition is recognized as an efficient, stable, and secure biometric method. However, when the cooperation with users is reduced under complex scenes, the collected iris images are prone to squint or defocus, which causes serious difficulties for iris preprocessing, especially iris location, and segmentation. To solve this problem, we propose a multitask deep learning model based on deep snake. Using the closed-loop prior conditions of the inner and outer boundaries of the iris itself, the active contour method is used to achieve accurate iris location, and semantic segmentation is carried out in the effective region. First, the initial iris location and segmentation results are obtained using the target detection module based on mask regions with convolutional neural networks, then the initial contour is located by poles. The iris boundary is represented by a set of ordered point coordinates through equal spacing sampling. The initial contour is transferred as input to the active contour fitting network based on deep snake to obtain accurate iris location results. Finally, the segmentation results are obtained by the internal and external boundary constraint models. In the segmentation process, efficient channel attention mechanism is introduced to improve the segmentation accuracy and make the network focus on the effective iris region. The SoftPool layer is used to reduce the loss of image detail information caused by pooling, so that the whole network is differentiable. The performance improvements of 3.26%, 3.9%, and 3.45% are obtained on the three squint iris datasets, respectively, which proves the effectiveness of the model in solving the squint iris problem and improves the accuracy and robustness of iris recognition in complex scenes.

Accurate iris segmentation and recognition using an end-to-end unified framework based on MADNet and DSANet

Due to their insufficient generalization ability, iris segmentation algorithms based on deep learning cannot accurately segment iris images without corresponding ground truth (GT) data. Moreover, prior to recognition, the segmented image requires normalization to reduce the influence of pupil deformation. However, normalization of nonconnected iris regions will introduce noise, thereby decreasing the recognition rate. This paper proposes an end-to-end unified framework based on deep learning that does not include normalization in order to achieve improved accuracy in iris segmentation and recognition. In this framework, a multiattention dense connection network (MADNet) and dense spatial attention network (DSANet) are designed for iris segmentation and recognition, respectively. Finally, numerous ablation experiments are conducted to demonstrate the effectiveness of MADNet and DSANet. Experiments on three employed databases show that our proposed method achieves the best segmentation and recognition performance on low-quality iris images without corresponding GT data.

Supervised Contrastive Learning and Intra-Dataset Adversarial Adaptation for Iris Segmentation.

Precise iris segmentation is a very important part of accurate iris recognition. Traditional iris segmentation methods require complex prior knowledge and pre- and post-processing and have limited accuracy under non-ideal conditions. Deep learning approaches outperform traditional methods. However, the limitation of a small number of labeled datasets degrades their performance drastically because of the difficulty in collecting and labeling irises. Furthermore, previous approaches ignore the large distribution gap within the non-ideal iris dataset due to illumination, motion blur, squinting eyes, etc. To address these issues, we propose a three-stage training strategy. Firstly, supervised contrastive pretraining is proposed to increase intra-class compactness and inter-class separability to obtain a good pixel classifier under a limited amount of data. Secondly, the entire network is fine-tuned using cross-entropy loss. Thirdly, an intra-dataset adversarial adaptation is proposed, which reduces the intra-dataset gap in the non-ideal situation by aligning the distribution of the hard and easy samples at the pixel class level. Our experiments show that our method improved the segmentation performance and achieved the following encouraging results: 0.44%, 1.03%, 0.66%, 0.41%, and 0.37% in the Nice1 and 96.66%, 98.72%, 93.21%, 94.28%, and 97.41% in the F1 for UBIRIS.V2, IITD, MICHE-I, CASIA-D, and CASIA-T.

Generalized Contact Lens Iris Presentation Attack Detection

The high accuracy of iris recognition for person identification has led to its deployment for a variety of applications ranging from border access to mobile unlocking to digital payment. In addition, the commercial success of mobile devices for iris image acquisition enables the easy acquisition of iris images both in an indoor controlled environment as well as an uncontrolled outdoor environment. At the same time, iris recognition systems can easily be attacked using wearable contact lenses. In the literature, several contact lens detection algorithms are proposed; however, the significant drawback is the generalizability under unseen testing domain images. In this research, a novel 3D contact lens iris presentation attack detection algorithm is developed and extensive experiments are performed. The experiments are performed using multiple challenging iris presentation attack databases including the IIITD and LivDet. For the evaluation, we have utilized the experimental protocols, which reflect in-the-wild settings for 3D contact lens iris presentation attack detection where the images belong to both controlled and adverse imaging conditions. The comparison with several state-of-the-art algorithms establishes the effectiveness of the proposed algorithm.

Towards more accurate iris recognition system by using hybrid approach for feature extraction along with classifier

<span lang="EN-US">Iris recognition become one of the most accurate and reliable steadfast human biometric recognition system of the decad. This paper presents an accurate framework for iris recognition system using hybrid algorithm in preprocess and feature extraction section. The proposed model for iris recognition with significant feature extraction was divided into three main levels. First level is having pre-processing steps which are necessary for the desired tasks. Our model deploys on three types of datasets such as UBIRIS, CASIA, and MMU and gets optimal results for performing activity. At last, perform matching process with decision based classifier for iris recognition with acceptance or rejection rates. Experimental based results provide for analysis according to the false receipt rate and false refusal amount. In the third level, the error rate will be checked along with some statistical measures for final optimal results. Constructed on the outcome the planned method provided the most efficient effect as compared to the rest of the approach.</span>

A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

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.

Occluded iris classification and segmentation using self-customized artificial intelligence models and iterative randomized Hough transform

A fast and accurate iris recognition system is presented for noisy iris images, mainly the noises due to eye occlusion and from specular reflection. The proposed recognition system will adopt a self-customized support vector machine (SVM) and convolution neural network (CNN) classification models, where the models are built according to the iris texture GLCM and automated deep features datasets that are extracted exclusively from each subject individually. The image processing techniques used were optimized, whether the processing of iris region segmentation using iterative randomized Hough transform (IRHT), or the processing of the classification, where few significant features are considered, based on singular value decomposition (SVD) analysis, for testing the moving window matrix class if it is iris or non-iris. The iris segments matching techniques are optimized by extracting, first, the largest parallel-axis rectangle inscribed in the classified occluded-iris binary image, where its corresponding iris region is crosscorrelated with the same subject’s iris reference image for obtaining the most correlated iris segments in the two eye images. Finally, calculating the iriscode Hamming distance of the two most correlated segments to identify the subject’s unique iris pattern with high accuracy, security, and reliability.

An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network.

Recently, deep learning approaches, especially convolutional neural networks (CNNs), have attracted extensive attention in iris recognition. Though CNN-based approaches realize automatic feature extraction and achieve outstanding performance, they usually require more training samples and higher computational complexity than the classic methods. This work focuses on training a novel condensed 2-channel (2-ch) CNN with few training samples for efficient and accurate iris identification and verification. A multi-branch CNN with three well-designed online augmentation schemes and radial attention layers is first proposed as a high-performance basic iris classifier. Then, both branch pruning and channel pruning are achieved by analyzing the weight distribution of the model. Finally, fast finetuning is optionally applied, which can significantly improve the performance of the pruned CNN while alleviating the computational burden. In addition, we further investigate the encoding ability of 2-ch CNN and propose an efficient iris recognition scheme suitable for large database application scenarios. Moreover, the gradient-based analysis results indicate that the proposed algorithm is robust to various image contaminations. We comprehensively evaluated our algorithm on three publicly available iris databases for which the results proved satisfactory for real-time iris recognition.

Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net.

Iris segmentation plays an important and significant role in the iris recognition system. The prerequisite for accurate iris recognition is the correctness of iris segmentation. However, the efficiency and robustness of traditional iris segmentation methods are severely challenged in a non-cooperative environment because of unfavorable factors, for instance, occlusion, blur, low resolution, off-axis, motion, and specular reflections. All of the above factors seriously reduce the accuracy of iris segmentation. In this paper, we present a novel iris segmentation algorithm that localizes the outer and inner boundaries of the iris image. We propose a neural network model called “Interleaved Residual U-Net” (IRUNet) for semantic segmentation and iris mask synthesis. The K-means clustering is applied to select saliency points set in order to recover the outer boundary of the iris, whereas the inner border is recovered by selecting another set of saliency points on the inner side of the mask. Experimental results demonstrate that the proposed iris segmentation algorithm can achieve the mean IOU value of 98.9% and 97.7% for inner and outer boundary estimation, respectively, which outperforms the existing approaches on the challenging CASIA-Iris-Thousand database.

A Post-Quantum Fuzzy Commitment Scheme for Biometric Template Protection: An Experimental Study

The proliferation and extensive acceptance of biometrics in many sectors is due to its merits over traditional password-based authentication. Several biometric template protection schemes have been proposed during the last decade to address the security and privacy issues related to biometrics. While error-correcting coding is required in the design of these schemes, it has been shown to degrade security and accuracy performance. In this study, a new post-quantum fuzzy commitment scheme (PQFC) for biometric template protection is designed, which is proved to be secure based on the Short Vector Problem (SVP) of the lattice. The present work provides the first systematic construction of biometric template protection that does not rely on error-correcting codes. To that purpose, a novel biometric-based PQFC authentication system based on a smartcard is created, with the biometric template protected by PQFC. Furthermore, biometric matching is done indirectly. The biometric-based PQFC authentication system is examined using an iris database of 108 individuals. According to the experimental data, the accuracy of iris recognition with PQFC is 99.1 percent, which is higher than the accuracy without PQFC, which is 97.4 percent. A False Accepting Rate (FAR) of 0% is achieved, while the False Rejection Rate (FRR) is 2.9%. The FRR can be adjusted by employing the best standard iris recognition algorithm. The execution time for the essential operations of PQFC and the storage requirements are estimated. Furthermore, the security of the proposed authentication system is analyzed, showing that the renewability, unlinkability, and irreversibility of the ISO/IEC 24745 standard are fulfilled without downgrading the accuracy. The overall performance of this work demonstrated that the PQFC is a promising technique for biometric template protection that is also suitable for the Internet of Things and cloud computing applications.

A new segmentation method for non-ideal iris images using morphological reconstruction FCM based on improved DSA

In any accurate iris recognition system segmentation of iris plays a vital role. The noise, specular reflections, eyelid/eyelash obstruction, and intensity inhomogeneities in an image make the segmentation more difficult. In this paper, a novel technique is proposed to segment the iris from images that are taken under uncooperative image conditions. The proposed method segments the image in two stages. Firstly, Morphological reconstruction fuzzy c-means clustering (MRFCM) based on an improved differential search algorithm is implemented before the segmentation step. The MRFCM can preserve image contours even in the presence of noise. Secondly, the iris is isolated from the undesired regions of an eye image by implementing geodesic active contours driven by a modified stopping criterion on the resultant images of the pre-segmentation step. The accuracy of the method presented has been tested on the databases such as CASIAv3-Interval, UBIRISv1, MMU1, IITDv1, and MICHE-I. The segmentation accuracy has been demonstrated and compared with other existing methods present in the literature. The obtained results are promising and the proposed model is outperformed the existing methods.

Periocular-Assisted Multi-Feature Collaboration for Dynamic Iris Recognition

Iris recognition has emerged as one of the most accurate and convenient biometric for person identification and has been increasingly employed in a wide range of e-security applications. The quality of iris images acquired at-a-distance or under less constrained imaging environments is known to degrade the iris recognition accuracy. The periocular information is inherently embedded in such iris images and can be exploited to assist in the iris recognition under such non-ideal scenarios. Our analysis of such iris templates also indicates significant degradation and reduction in the region of interest, where the iris recognition can benefit from a similarity distance that can consider importance of different binary bits, instead of the direct use of Hamming distance in the literature. Periocular information can be dynamically reinforced, by incorporating the differences in the effective area of available iris regions, for more accurate iris recognition. This article presents such a periocular-assisted dynamic framework for more accurate less-constrained iris recognition. The effectiveness of this framework is evaluated on three publicly available iris databases using within-dataset and cross-dataset performance evaluation, e.g., improvement in the recognition accuracy of 22.9%, 10.4% and 14.6% on three databases under both the verification and recognition scenarios.

Noisy Iris Recognition Based on Deep Neural Network

Iris recognition is one of the Biometric systems used for persons identification based on their special iris traits, which are unique featuresfor each individual. It is clear that the progress in deep learning show how efficient the extracted features from convolutional neural networks (CNNs) to describe the complex image patterns. However, the influence of noise is a serious problem in most image processing systems. It may ariseto the iris recognition systems due to environmental conditions that can affects the features extracted from the iris images. Hence, the objective of this paper is to study the performance of CNNs based Deep learning (Alex net, Vgg16 and Vgg19) when used for iris recognition with the presence of noise and compares it with Masek algorithm. Simulation results reveal that using the deep learning greatly improves iris recognition accuracy for Alex CNN. We achieve 100%, 100%, 88.9% for interval, lamp and twins datasets respectively.