Iris Recognition Research Articles

Fusion of Gabor filter and steerable pyramid to improve iris recognition system

<span>Iris recognition system is a technique of identifying people using their distinctive features. Generally, this technique is used in security, because it offers a good reliability. Different researchers have proposed new methods for iris recognition system to increase its effectiveness. In this paper, we propose a new method for iris recognition based on Gabor filter and steerable pyramid decomposition. It’s an efficient and accurate linear multi-scale, multi-orientation image decomposition to capture texture details of an image. At first, the iris image is segmented, normalized and decomposed by Gabor filter and steerable pyramid method. Multiple sub-band are generated by applying steerable pyramid on the input image. High frequency sub-band is ignored to eliminate noise and increase the accuracy. The method was validated using CASIA-v4 (Chinese Academy of Sciences Institute of Automation), IITD (</span><span>Indian Institute of Technology Delhi) and UPOL (University of Phoenix Online) databases. The performance of the proposed method is better than the most methods in the literature. The proposed algorithm provides accuracy of 99.99%. False acceptance rate (FAR), equal error rate (EER) and genuine acceptance rate (GAR) have also been improved.</span>

Biometrics in the Era of COVID-19: Challenges and Opportunities

Since early 2020, the COVID-19 pandemic has had a considerable impact on many aspects of daily life. A range of different measures have been implemented worldwide to reduce the rate of new infections and to manage the pressure on national health services. A primary strategy has been to reduce gatherings and the potential for transmission through the prioritisation of remote working and education. Enhanced hand hygiene and the use of facial masks have decreased the spread of pathogens when gatherings are unavoidable. These particular measures present challenges for reliable biometric recognition, e.g., for facial-, voice- and hand-based biometrics. At the same time, new challenges create new opportunities and research directions, e.g., renewed interest in non-constrained iris or periocular recognition, touch-less fingerprint- and vein-based authentication and the use of biometric characteristics for disease detection. This article presents an overview of the research carried out to address those challenges and emerging opportunities.

An Efficient Automated Iris Recognition Classification Model Based on Different Convolutional Neural Networks and Transfer Learning.

An Efficient Automated Iris Recognition Classification Model Based on Different Convolutional Neural Networks and Transfer Learning.

자가 진단을 위한 스마트폰 적용 LED 홍채 렌즈 연구

Digital health services tend to go beyond simple health management and auxiliary means to advanced disease monitoring. The use of apps related to particular diseases such as diabetes and the circulatory system is a large increasing every year. In particular, there is connected to the brain and nervous system tissues so it is possible to diagnose the disease state of the related tissue from all the changes in the iris image. Although an iris glasses camera module was developed in the previous study, there was a limit in user convenience in the method of acquiring images using the USB communication method. In addition, if the disease is read with an opaque iris image when taking an iris photograph with a smartphone, the patient’s disease may be missed as a result of no disease, so it is important to obtain a clear iris image. This research proposed an iris recognition lens implementation of a small and can be easily carried and designed considering the safety of patients with high-definition iris image capture. In this study, an LED iris lens was designed and manufactured can iris recognition to show the crown of the autonomic nervous system. It is necessary to verify whether the developed LED iris lens can perform to show the crown of the autonomic nervous system. Measurements are classfied in two ways iris clarity through smartphones, iris clarity through Test Tool, and the number of attempts to secure a clear iris image. The development lange of the Test Tool is Python, and the Average Square Shrinking algorithm was applied to detect autonomic neuropathy in the iris. When verifying the iris, the iris data set provided by CASIA(China’s Biological Research Institute)was used as reference data. As a result of the test, The conventional app for iris recognition can obtain at most 12% of clarity which is not enough to diagnosis the diseases while using the LED iris lens on the smartphone gives 91% of the iris brightness. Additionally, the number of trials for iris recognition was only once while the conventional method needs at least 8 times to get a clear iris image. As a result of verifying the iris image by developing an LED iris lens through this study, it was possible to secure a clear iris image at the level of disease diagnosis. A clear iris reduces diagnostic errors and contributes sufficiently to patient safety. There is a lot of interest in mobile health apps for self-diagnosis. However, it is difficult to find useful App. for self-diagnosis. After applying the LED iris lens developed through this study, it is judged that it will be of practical help to disease prevention if the health status of the iris is checked periodically.

Design of Voice Command Access Using Matlab

Abstract: This particular project is mainly deals with security and access to a device. As the present generation the technology was increasing rapidly. The security for the electronic devices is also increasing highly. There are different types of security was implemented for the electronic devices. They are: Biometric, Iris Scan, Facial Recognition and Voice Recognition We preferably think Voice Recognition is more secured when compared. So we are designing a voice Access Module. For which we are using Audio processing subject on a lighter note. The central idea of the project is to use MATLAB software and record few people audio and store them as a data- base. Now the task to be done by design is when a person tries to open system with his voice command, the design need to verify that particular audio with the database created. The design allows the person only when he is one of the authorized persons, in else case it recognizes that the one is an intruder.

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.

Dynamic iris inner edge location method based on distance-regularized level set

Iris localization is an important part of iris recognition. Aiming at the influence of irregular pupil circle and illumination inequality in dynamic iris localization in actual scene, a dynamic iris inner edge location method based on distance regularized level set model was proposed. According to the geometric features of iris, the pupil and iris are coarsely located through the secondary projection of binary image, and the initial contour of evolution of horizontal set is determined. Accurate positioning of the inner edge of iris is achieved by the improved distance regularized level set model of edge indicator function. Iris images Database were respectively experimented with this paper and another improved distance regularized level set model, and the positioning effect was evaluated by using the Jakard similarity coefficient. The results show that the distance regularized level set model based dynamic iris internal edge localization algorithm proposed in this paper can significantly reduce the number of iterations and positioning time of the localization algorithm on the basis of ensuring the positioning accuracy,which provides a theoretical basis and technical reference for the application of iris recognition technology.

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.

Efficient, accurate and fast pupil segmentation for pupillary boundary in iris recognition

Iris recognition is a robust biometric system—user-friendly, accurate, fast, and reliable. This biometric system captures information in a contactless manner, making it suitable for use during the COVID-19 pandemic. Despite its advantages such as high security and high accuracy, iris recognition still suffers from pupil deformation, motion blur, eyelids blocking, reflection occlusion and eyelashes obscure. If the pupillary boundary is not accurately segmented, iris recognition may suffer tremendously. Moreover, reflections in iris image may lead to an incorrect pupillary boundary segmentation. The segmentation accuracy can also be affected and reduced because of the presence of an unwanted noise created by the motion blur effect in iris image. Additionally, the pupillary boundary might change from circular shape to uneven or irregular shape because of the interference and obstruction in pupil region. Therefore, this work is carried out to determine an accurate, efficient and fast algorithm for the segmentation of pupillary boundary. First, the iris image is pre-processed with Wiener filter. Next, the respective iris image is assigned with a specific threshold. After that, the pixel property in iris image is computed to determine the pupillary boundary coordinates which are acquired from the measured pixel list and area in iris image. Finally, morphological closing is used to remove reflections in the inner region of pupil boundary. All experiments are implemented with CASIA v4 database and Matlab R2020a.

A proposed framework for face - iris recognition system using enhanced mayfly algorithm

Fused biometrics systems have proven to solve some problems associated with unimodal systems but also face challenges in various aspects of their implementation such as difficulty in design, user acceptance is quite low, and the performance tradeoff. This framework tends to address some of these implementation challenges by using an enhanced mayfly algorithm, a modification of the existing mayfly algorithm that was recently proposed, as feature selection. Mayfly algorithm combines advantages of particle swarm optimization, genetic algorithm, and firefly algorithm, simulated in different experiments using varied benchmark function on conventional mayfly algorithm all tested to be capable of optimization, but despite its capabilities, some limitations such as slow convergent or premature convergent rate and possible imbalance between exploration and exploitation still remain unresolved, which necessitated enhancement for better performance. This framework will enhance the existing mayfly algorithm by expanding the search space which limited the ability of the conventional mayfly algorithm to be used to solve high-dimensional problem spaces such as feature selection and modify the selection procedure to model the attraction process as a deterministic process, that will be used for the feature selection procedure on fused face –iris recognition system. This will increase the capabilities of the mayfly algorithm and in turn, increase the recognition accuracy, and reduced the false acceptance rate, false rejection rate, and time complexity of the fused face–iris recognition system.

Twitter, Facebook, and Google all have a trust problem

Being watched means much more than being seen. This forum investigates information flows of sensing culled from sources as diverse as temperature check and iris scans to sound and movement sensors across terrains. After Veillance discusses how these systems distribute risk unevenly and shape the lives of populations across the globe. --- Sareeta Amrute, Editor

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.

Performance boosting of image matching-based iris recognition systems using deformable circular hollow kernels and uniform histogram fusion images

Identification of people using different biometric data is becoming more important in network society. Biometrics include voice, ears, palms, fingerprints, faces, iris, retina, and hand shapes. Among these features, iris detection gets more attention because each iris type is unique and does not change throughout life. In this study, an iris recognition framework is proposed using deformable circular hollow kernels and uniform histogram fusion images (UHFIs). This system introduces two different approaches for the iris recognition: one with image matching without machine learning and the other one with machine learning. In the first approach, image matching through Gabor features is employed. After getting the circularly cropped UHFI, Gabor features are extracted and employed in the image matching-based recognition system. In addition, a normalized cross-correlation coefficient is used as a similarity metric to compare the Gabor feature vectors. In the second approach, Gabor feature images are extracted and employed in deep learning (DL). According to the experimental results, the proposed system reaches around 89% accuracy on MMU1, 86% accuracy on MMU2, and about 50% accuracy on the CASIAV3 and CASIAV4 datasets when no machine learning is employed. Extensive benchmarking results also indicate that the proposed system boosts the performance of conventional systems by at least around 40% in terms of accuracy in the absence of machine learning. However, in the presence of machine learning, experimental results show that the proposed method with DL achieves an accuracy of up to 100% for MMU1, MMU2, CASIAV3, and CASIAV4 datasets.

LiSeNet: multitask lightweight segmentation network for accurate and complete iris segmentation

The demand for applying the iris segmentation model on mobile devices has been growing rapidly. Most current segmentation networks have an enormous amount of parameters, hence unsuitable for mobile devices, while other small memory footprint models follow the spirit of classification networks and ignore the inherent characteristic of segmentation. To address the challenge, we propose a lightweight segmentation network (LiSeNet) for iris segmentation of noisy images. Unlike previous studies that only focus on improving the accuracy of segmentation masks, LiSeNet can simultaneously obtain segmentation masks, parameterized pupillary and limbic boundaries of the iris, further enabling CNN-based iris segmentation to be applied in any regular iris recognition systems. We first propose a multiscale concatenate (MSC) Block, which connects multiple sizes of convolution kernels in a dense manner, gradually reduces the dimension of feature maps and uses the aggregation of them for image representation. Based on the MSC block, we develop a two-stage refinement encoder to aggregate discriminative features through subnetwork feature reuse and substage feature reassess, thus obtaining a sufficient receptive field and enhancing the model learning ability. To exploit object contextual information more efficiently, we further devise a grouped spatial attention to emphasize the important features and suppress irrelevant noises through a gating mechanism in the decoder. Extensive experiments on three challenging iris datasets show that LiSeNet, without any complicated postprocessing, achieves competitive or state-of-the-art performance with only 2.2M parameters, being 14 × smaller than the previous best method. Code will be publicly available.

Perancangan Alat Pendeteksi Iris Mata Menggunakan Metode Wavelet Filter

The development of biometric technolgy is growing so that many recognition systems use biometrics in the form of physical characteristics and behavioral characteristics. Physical characteristics such as fingerprints, retina of the eye, face, and iris. While the behavioral characteristics include signatures, gait, and voice. One of the biometric parts used as an identifier is iris recognition using mathematical techniques. The iris of the eye has a unique and distinct pattern for each individual with stable identification and tends to remain unchanged. This study aims to design a tool that is able to detect a person's iris with a feature extraction method, namely Gabor wavelet with a classification method, namely K-Nearest Neighbor (K-NN). The results of the experiments carried out, the system succeeded in recognizing the iris image according to the selected image. However, if the selected image is not in the database, the results will show the name of the image that has the closest predictive value.

Ocular Biometrics with Low-Resolution Images Based on Ocular Super-Resolution CycleGAN

Iris recognition, which is known to have outstanding performance among conventional biometrics techniques, requires a high-resolution camera and a sufficient amount of lighting to capture images containing various iris patterns. To address these issues, research is actively conducted on ocular recognition to include a periocular region in addition to the iris region, which also requires a high-resolution camera to capture images, indicating limited applications due to costs and size limitation. Accordingly, this study proposes an ocular super-resolution cycle-consistent generative adversarial network (OSRCycleGAN) for ocular super-resolution reconstruction, and additionally proposes a method to improve recognition performance in case that ocular images are acquired at a low-resolution. The results of the experiment conducted using open databases, namely, CASIA-iris-Distance and Lamp v4, and IIT Delhi iris database, showed that the equal error rate of recognition of the proposed method was 3.02%, 4.06% and 2.13% for each database, respectively, which outperformed state-of-the-art methods.

Iris Recognition Method Based on Parallel Iris Localization Algorithm and Deep Learning Iris Verification.

Biometric recognition technology has been widely used in various fields of society. Iris recognition technology, as a stable and convenient biometric recognition technology, has been widely used in security applications. However, the iris images collected in the actual non-cooperative environment have various noises. Although mainstream iris recognition methods based on deep learning have achieved good recognition accuracy, the intention is to increase the complexity of the model. On the other hand, what the actual optical system collects is the original iris image that is not normalized. The mainstream iris recognition scheme based on deep learning does not consider the iris localization stage. In order to solve the above problems, this paper proposes an effective iris recognition scheme consisting of the iris localization and iris verification stages. For the iris localization stage, we used the parallel Hough circle to extract the inner circle of the iris and the Daugman algorithm to extract the outer circle of the iris, and for the iris verification stage, we developed a new lightweight convolutional neural network. The architecture consists of a deep residual network module and a residual pooling layer which is introduced to effectively improve the accuracy of iris verification. Iris localization experiments were conducted on 400 iris images collected under a non-cooperative environment. Compared with its processing time on a graphics processing unit with a central processing unit architecture, the experimental results revealed that the speed was increased by 26, 32, 36, and 21 times at 4 different iris datasets, respectively, and the effective iris localization accuracy is achieved. Furthermore, we chose four representative iris datasets collected under a non-cooperative environment for the iris verification experiments. The experimental results demonstrated that the network structure could achieve high-precision iris verification with fewer parameters, and the equal error rates are 1.08%, 1.01%, 1.71%, and 1.11% on 4 test databases, respectively.

Perancangan Aplikasi Absensi Karyawan Dengan Deteksi Wajah Menggunakan Metode Eigenface

Attendance is one of the most important repetitive transactions, because it is related to the productivity of employees and is one of the indicators of controlling human resources (HR) which aims to increase the potential of human resources and is used for efficiency. Current technological developments make it possible to create a system that can assist humans in recognizing a digital image. One area that is currently being developed is pattern recognition. This technology identifies a person's special physical characteristics. Examples of pattern recognition, for example, are face recognition, iris recognition, and fingerprint recognition. specific databases. Broadly speaking, the process of facial recognition is that the webcam camera captures the face. Then obtained a value of R, G, B. By using the initial processing, after that the facial processing stage is carried out using the eigenface method. In this eigenface method there are several core stages, namely: converting faces into matrices, calculating the FlatVector average, determining the eigenface values ??and carrying out the face identification process by looking for the eigenface values ??that are close to it. One of these facial recognition can be developed to become an attendance application that can be applied in the company to prevent manipulation of absenteeism by employees.

Anti-spoofing detection based on eyeblink liveness testing for iris recognition

Iris pattern is the most stable biometric trait for personal identification. It is the only traits that can’t be used after a person death. Despite its stability and difficulty to spoof, it has found that presenting a high quality image of an iris one can spoof and gain an access. Furthermore, the use of video frames of an authorized personnel and the use of 3D models can cheat the system. This study aimed at presenting a solution to this problem by testing the liveness of an eye being scanned by an access control device. The algorithm works by additional process of detecting an eyeblink and background subtraction and correlation to assume liveness. For one to gain access, first an iris is scanned and identified, secondly if this iris is in the database before providing an access, an eyeblink is also sensed. If eyeblink is sensed an access is granted otherwise access is denied. An algorithm has been developed in MATLAB adopting an adaptive Canny method for edge detection. The proposed algorithm validates the user being scanned by two stages which are; Eyeblink detection, background subtraction and correlation. Testing on standard datasets of ZJU Eyeblink, ACASIA v3 and the TalkingFace databases showed show 96.47% accuracy.

Biometric Iris Recognition System For Identity Verification

Individual identity authentication is currently one of the most important components in a wide range of applications. As a consequence, the iris-based biometric technology has piqued the curiosity of many in this field. This high level of interest originates from its high level of accuracy and potentially promising applications. This article focuses on the various studies that have been done on the subject, as well as the various approaches that make up the biometric system based on iris recognition. Methodologies or methods for segmentation, feature extraction, and matching are given special attention. For matching, we employ two different classification methods in our research. ANFIS and k nearest neighbour are the strategies. The iris is located using morphological methods.