Iris Feature Research Articles

Design of 1300 nm spectral domain optical coherence tomography angiography system for iris microvascular imaging

Developing a high-resolution non-invasive optical coherence tomography angiography (OCTA) method for iris vasculature imaging is essential for diagnosing a wide range of ocular pathologies. However, the current iris-OCTA devices are still limited in imaging quality and penetration depth for dark-colored eyes ranging from brown to dark brown. A spectral domain iris-OCTA system is presented in this paper incorporating a 1300 nm wavelength for deeper tissue penetration, a linear-wavenumber spectrometer for better detection sensitivity, and an iris scan objective lens for better optical focusing across the entire iris over a 12 × 12 mm2 scan field. The −6 dB fall-off range is ∼3 mm, and the maximum sensitivity fall-off is −28.57 dB at 6.94 mm. The axial resolution is 15.1 ± 3.2 μm. The 40 mm focal-length iris scan objective is optimized based on the ocular parameters from 100 Asian participants’ left eyes, and it has a diffraction-limited lateral resolution (14.14 μm) for the iris, in general. OCT distortions were calibrated based on the average ocular parameters, and the maximum residual distortions in both the lateral and axial directions were <0.1 mm (2.0%) for all of the eyes. A pilot study on a constricted pupil was performed to demonstrate high-contrast, wide-field en face iris microvascular imaging by either a horizontal or vertical fast-scan protocol in a dark brown eye. The iris vessels are radially aligned, and each vessel is more visible when it has an angle of ∼65°–90° with respect to the fast-scan direction. A new circular fast-scan protocol could improve image quality for better visualization of the iris features or integration with image-registration algorithms and an eye-tracking system for eye-motion compensation.

Second-Order Statistical Methods GLCM for Authentication Systems

For many uses, biometric systems have gained considerable attention. Iris identification was One of the most powerful sophisticated biometrical techniques for effective and confident authentication. The current iris identification system offers accurate and reliable results based on near-infrared light (NIR) images when images are taken in a restricted area with fixed-distance user cooperation. However, for the color eye images obtained under visible wavelength (VW) without collaboration among the users, the efficiency of iris recognition degrades because of noise such as eye blurring images, eye lashing, occlusion, and reflection. This work aims to use the Gray-Level Co- occurrence Matrix (GLCM) to retrieve the iris's characteristics in both NIR iris images and visible spectrum. GLCM is second-order Statistical-Based Methods for Texture Analysis. The GLCM-based extraction technology was applied after the preprocessing method to extract the pure iris region's characteristics. The Energy, Entropy, Correlation, homogeneity, and Contrast collection of second-order statistical features are determined from the generated co-occurrence matrix, Stored as a vector for numerical features. This approach is used and evaluated on the CASIA v1and ITTD v1 databases as NIR iris image and UBIRIS v1 as a color image. The results showed a high accuracy rate (99.2 %) on CASIA v1, (99.4) on ITTD v1, and (87%) on UBIRIS v1 evaluated by comparing to the other methods

An Efficient Human Identification Through Iris Recognition System

As a part of a growing information society, nowadays the issue of security is more crucial than ever. In order to achieve high level of security, the potential of accurately recognize subjects based on their unique measurable physiological or behavioral characteristics has been receiving an increased concern by the research and development community. As biometrics has advanced, iris has been considered a preferred trait because unique pattern texture, lifetime stability, and regular shape contribute to good segmentation and recognition performance. The incredible uniqueness of iris patterns as well as the ability to capture iris images non-invasively has motivated us to develop automated system for iris recognition based on 2-D iris images. The 2DPCA (two-dimensional Principal Component Analysis) and GA (Genetic Algorithm) have been used as feature extraction and feature selection techniques for reducing the dimensionality of iris features without the loss of relevant Information. The Back Propagation Neural Network (BPNN) is implemented using Levenberg–Marquardt’s learning rule for iris recognition. The experimental results illustrated that the 2DPCA-GA achieved a high classification accuracy of 96.40 %.

Deblurring of noisy iris images in iris recognition

Iris recognition used the iris features to verify and identify the identity of human. The iris has many advantages such as stability over time, easy to use and high recognition accuracy. However, the poor quality of iris images can degrade the recognition accuracy of iris recognition system. The recognition accuracy of this system is depended on the iris pattern quality captured during the iris acquisition. The iris pattern quality can degrade due to the blurry image. Blurry image happened due to the movement during image acquisition and poor camera resolution. Due to that, a deblurring method based on the Wiener filter was proposed to improve the quality of iris pattern. This work is significant since the proposed method can enhance the quality of iris pattern in the blurry image. Based to the results, the proposed method improved the quality of iris pattern in the blurry image. Moreover, it recorded the fastest execution time to improve the quality of iris pattern compared to the other methods.

An Improved Iris Recognition Method Based on Wavelet Packet Transform

Wavelet packet decomposition transformation is an extension of wavelet transformation, which can achieve more refined decomposition and get more wavelet packet subgraphs. In order to improve the recognition accuracy of iris, an improved iris recognition algorithm based on wavelet packet transform is proposed. First locate and normalize the inner and outer edges of the iris, then obtain the wavelet packet subgraphs through wavelet packet decomposition, calculate the coefficients of each subgraphs to obtain the iris feature vectors, and then calculate the Hamming distances of the corresponding feature vectors of the two iris images, according to different subgraphs. The calculated coefficients are identified by the weighted Hamming distance classifier.

Whether normalized or not? Towards more robust iris recognition using dynamic programming

Iris recognition is one of the most promising fields in biometrics due to more accurate, convenient and low-cost. However, it is still a challenging task for application in practical complex scenarios. More attention have been paid on non-ideal iris segmentation and cross-system feature extraction in recent years. In order to solve the issues, this paper investigates a novel non-normalized preprocessing method based on dynamic path search for iris segmentation. Meanwhile, we employ a deep convolution network (DCNN) based on partial convolution operators to extract iris features. Through benchmark experiments on two public iris datasets CASIA-Iris-Thousand (CASIA) and IIT Delhi Iris Dataset (IITD), we achieve the significant and encouraging results, which demonstrate the effectiveness of the proposed methods. More importantly, we prove that using iris segmentation images without normalization may be a better choice when exploring iris recognition solutions based on deep learning.

A Modified Chaotic Binary Particle Swarm Optimization Scheme and Its Application in Face-Iris Multimodal Biometric Identification

In order to improve the recognition rate of the biometric identification system, the features of each unimodal biometric are often combined in a certain way. However, there are some mutually exclusive redundant features in those combined features, which will degrade the identification performance. To solve this problem, this paper proposes a novel multimodal biometric identification system for face-iris recognition.It is based on binary particle swarm optimization. The face features are extracted by 2D Log-Gabor and Curvelet transform, while iris features are extracted by Curvelet transform. In order to reduce the complexity of the feature-level fusion, we propose a modified chaotic binary particle swarm optimization (MCBPSO) algorithm to select features. It uses kernel extreme learning machine (KELM) as a fitness function and chaotic binary sequences to initialize particle swarms. After the global optimal position (Gbest) is generated in each iteration, the position of Gbest is varied by using chaotic binary sequences, which is useful to realize chaotic local search and avoid falling into the local optimal position. The experiments are conducted on CASIA multimodal iris and face dataset from Chinese Academy of Sciences.The experimental results demonstrate that the proposed system can not only reduce the number of features to one tenth of its original size, but also improve the recognition rate up to 99.78%. Compared with the unimodal iris and face system, the recognition rate of the proposed system are improved by 11.56% and 2% respectively. The experimental results reveal its performance in the verification mode compared with the existing state-of-the-art systems. The proposed system is satisfactory in addressing face-iris multimodal biometric identification.

Light microscopic iris classification using ensemble multi-class support vector machine.

Similar to other biometric systems such as fingerprint, face, DNA, iris classification could assist law enforcement agencies in identifying humans. Iris classification technology helps law-enforcement agencies to recognize humans by matching their iris with iris data sets. However, iris classification is challenging in the real environment due to its invertible and complex texture variations in the human iris. Accordingly, this article presents an improved Oriented FAST and Rotated BRIEF with Bag-of-Words model to extract distinct and robust features from the iris image, followed by ensemble multi-class-SVM to classify iris. The proposed methodology consists of four main steps; first, iris image normalization and enhancement; second, localizing iris region; third, iris feature extraction; finally, iris classification using ensemble multi-class support vector machine. For preprocessing of input images, histogram equalization, Gaussian mask and median filters are applied. The proposed technique is tested on two benchmark databases, that is, CASIA-v1 and iris image database, and achieved higher accuracy than other existing techniques reported in state of the art.

An Identity-Based Secure and Optimal Authentication Scheme for the Cloud Computing Environment

Security is a critical issue in cloud computing (CC) because attackers can fabricate data by creating, copying, or deleting data with no user authorization. Most of the existing techniques make use of password-based authentication for encrypting data. Password-based schemes suffer from several issues and can be easily compromised. This paper presents a new concept of hybrid metaheuristic optimization as an identity-based secure and optimal authentication (HMO-ISOA) scheme for CC environments. The HMO-ISOA technique makes use of iris and fingerprint biometrics. Initially, the HMO-ISOA technique involves a directional local ternary quantized extrema pattern–based feature extraction process to extract features from the iris and fingerprint. Next, the features are fed into the hybrid social spider using the dragon fly algorithm to determine the optimal solution. This optimal solution acts as a key for an advanced encryption standard to encrypt and decrypt the data. A central benefit of determining the optimal value in this way is that the intruder cannot determine this value. The attacker also cannot work out which specific part of the fingerprint and iris feature values are acted upon as a key for the AES technique. Finally, the encrypted data can be saved in the cloud using a cloud simulator. Experimental analysis was performed on five fingerprint and iris images for a man-in-the-middle attack. The simulation outcome validated that the presented HMO-ISOA model achieved better results compared with other existing methods.

3D Model-Based Gaze Tracking Via Iris Features With a Single Camera and a Single Light Source

Traditional 3D gaze estimation methods are usually based on the models of pupil refraction and corneal reflection. These methods typically rely on multiple light sources. The 3D gaze can be estimated using single-camera-single-light-source systems only when certain user-dependent eye parameters are available a priori, which is rarely the case. This article proposes a 3D gaze estimation method which works based on iris features using a single camera and a single light source. User-dependent eye parameters involving the iris radius and the cornea radius are user-calibrated. The 3D line-of-sight is estimated from the optical axis and the positional relationship between the optical axis and the visual axis, and then optimized using a binocular stereo vision model. The feasibility and robustness of the proposed method are assessed by simulations and practical experiments. The system configuration required by the method is simpler than that required by the state-of-the-art methods, which shows significant potential value, especially with regard to mobile device applications.

A Fuzzy-Based Multi-Modal Architectural Model for Electioneering in Nigeria

The 2015 election has been adjured to be the best in the history of elections in Nigeria, thanks to the deployment of information technology by the Independent National Electoral Commission (INEC). In reality, the election was not as flawless as many people believe. There were lots of problems associated with it, particularly in the areas of verification and authentication of eligible voters. This work examines these problems and proposed a fuzzy-based multi-modal architectural model for future elections in contrast to the unimodal model currently in use. The architecture is based on the extraction of the features of fingerprint and iris of the prospective voters as opposed to only fingerprint currently in use. Drawing from the strength of fingerprint and Iris features discussed in the work, a robust architecture supported by the fuzzy-based mathematical model was developed for future elections. Further discussed were the advantages of the proposed architecture over the current INEC method of voter verification and authentication.

THE EFFECT OF VISCOELASTIC ON SHORT TERM STABILITY OF TORIC INTRA OCULAR LENSES

The advancement in intraocular lens (IOL) design in addition to surgical procedures has elevated the expectations of patients for exceptional postoperative vision and complete spectacle independence after cataract surgery.Available toric IOLs:Standard toric IOLs are available in cylinder powers of 1.5D to 6.00D.they are usually intended for regular corneal astigmatism in arrange from 0.75D to 4.75DApplication of viscoelastic substances in cataract surgery aims mainly to prevent loss of the corneal endothelium. This purpose is achieved through deepening of the anterior chamber, protection of the endothelial cells from surgical trauma mechanically and coating of IOL. To simplify, Ovds can be divided into 4 broad categories: I) Dispersives, II) Cohesives, III) Combination agents and IV) Visco-adaptives.Alcon's VERION Digital Marker:It is atype of digital marking used for aligning of the toric IOL. The VERION system matches the preoperative photograph with the eye intraoperatively using scleral vessels, limbal vessels and iris features.

Cancelable Iris template generation by aggregating patch level ordinal relations with its holistically extended performance and security analysis

Nowadays, biometric-based authentication is gaining immense popularity due to the widespread usage of digital activities. Among various biometric traits, the iris is one of the most discriminative, accurate, and popularly used biometrics. However, due to its immutable nature, it is highly vulnerable to adversarial attacks if stolen and thus poses a severe security threat. Here, in this work, we propose a cancelable iris biometric authentication system that stores a transformed version of the original iris template and thus enables cancelation and re-enrolment in case if the original template is stolen. Firstly, for extracting discriminative iris features, we have proposed a novel deep architecture based on aggregation learning. This deep architecture makes use of qualitative measure (ordinal measure), unlike popularly used quantitative measures. The usage of ordinal measures in this work enables to encode distinctive iris features quite well. Later generated iris features are protected using state-of-the-art two representative cancelable biometric techniques, namely BioHashing and 2N discretized BioPhasor. Finally, in order to justify the efficacy of the proposed architecture, we have presented rigorous and holistic security analysis. To the best of our knowledge, this is the first work that has presented such an in-depth analysis of any deep network in the context of cancelable iris biometrics. Experimental results over four datasets viz. CASIA-V3 Interval, CASIA-Lamp, IITD, and IITK demonstrate the efficacy of the proposed framework in terms of security and accuracy. Further, for better network explainability, we have also performed layer-specific heatmap and feature map analysis to ascertain what exactly our novel deep architecture is learning.

Heterogeneous iris certification method with universal sensors and safety output based on multisource fusion features and entropy labels of lightweight samples

Current iris recognition technology faces practical difficulties. For example, the unsteady morphology of a heterogeneous iris is generated by a variety of different devices and environments. The existing iris data sets have difficulty meeting the requirements of learning methods under a single deep learning framework. Research on iris recognition security has limited response to results stealing. Therefore, we propose a one-to-one heterogeneous iris certification method with universal sensors and safety output based on multisource fusion features and entropy labels of lightweight samples. This method is based on the classic convolutional neural network structure, and a lightweight neural network certification structure is designed. At first, convert the constrained multistate iris image into the digital features based on statistical learning and multisource feature fusion mechanism. The information entropy of the iris feature label is used to set the iris entropy feature category label and design a certification function that meets the requirements of different acquisition sensors according to the category label to obtain the certification result. Through the result encryption output module, the security output is achieved between the user and the certification result, and measures can be taken in time to confirm the stealing attack to improve the security of the output of certification result. As the requirement for the number and quality of irises changes, the category labels in the certification function are dynamically adjusted using a feedback learning mechanism. Three different acquisition sensors in the JLU iris library are used to do the experiments. The results prove that, for lightweight constrained multistate irises, the above mentioned problems are ameliorated to a certain extent by this method.

Applications of Human Biometrics in Digital Image Processing

Digital image processing is the trending topic of research in recent time and big amount of research work related to Biometric features is done and currently it achieved good amount of accuracy. Biometric features is used for security, verification and recognition purpose. This paper is a showcase of how security systems can be developed by using biometric features of human like face, fingerprint and iris, etc. It can be used for the purpose of identification, recognition and Authentication and it is also applicable for making software for image preparation in bioscience laboratories that make use of scanned or digitally photographed images. The widespread use of such image processing techniques using photography and microscope imaging across the natural science with particular attention being paid to research in cell and molecular bioscience. This paper is a review of various methods trending to the field of biometric applications on biotechnologies.

An Efficient Multi-Modal Biometric Sensing and Authentication Framework for Distributed Applications

Multimodal biometrics is an emerging technology for distributed data security. Single and multi-user data authentication plays a vital role in commercial or e-governance applications. Many approaches have been implemented in literature to secure the single user data using biometric security systems. Most of these systems are based on static initialization parameters and fixed multi-modal biometric features for data authentication. Also, traditional multi-modal biometric based data authentication schemes are independent of dynamic variation in integrity verification. In order to overcome these problems, a new multi-user based multi-modal authentication framework is designed and implemented on large image data types. In this framework, different biometric features such as IRIS, facial and fingerprint features are used to find the unique integrity of user for data authentication and security process. A new integrity computational algorithm and encryption technique are implemented to provide the strong data integrity verification and data security in distributed applications. Experimental results show that the proposed multi-modal integrity-based encryption model has nearly 7% of computational integrity bit change and 5% of runtime on large dataset.

Iris template protection using double bloom filter based feature transformation

The primary focus of the bloom filter based iris feature transformations are template protection, data compression, response time of matching and the accuracy of matching. The increase in size of the code word of the bloom filter based feature transformation results in an exponential increase in the size of the transformed iris template and it degrades the data compression ratio, response time of matching and the accuracy of matching. This study presents a double bloom filter based iris feature transformation to reduce the exponential increase in the size of the transformed iris template. The number of bits set to one in the bloom filter based feature transformation is very less and it increases the false acceptance rate. The total bits set to one in the double bloom filter based feature transformation is double that of the bloom filter based transformation and it enhances the accuracy of matching of the transformed iris template. Experimental analysis exhibits that the double bloom filter based feature transformation enhances the data compression ratio, template protection, response time of matching and the accuracy of matching. The double bloom filter transformation reduces the size of the transformed iris template and increases the number of bits set to one there by it enhances the data compression ratio, template protection, response time of matching and the accuracy of matching.

Iris Liveness Detection Using Fusion of Domain-Specific Multiple BSIF and DenseNet Features.

In the past few years, some fusion-based approaches have been proposed to constitute discriminatory features for iris liveness detection. However, several methods exist in the literature for iris feature extraction and, thus, identifying an optimal composite of such features is still a vital challenge. This article also proposes a score-level fusion of two distinct domain-specific features, i.e., multiple binarized statistical image feature (BSIF) and DenseNet-based features. However, instead of randomly scrutinizing such features, statistical tests are executed on six predominant iris features to identify the optimal feature set to combine. Particularly, this work emphasizes textured-lens-based presentation attacks and aims to identify the type of contact lenses within the iris samples. The experimental analysis depicts that the domain-specific features substantially outperform the generic features while discriminating live iris from the artifacts. Furthermore, the proposed fusion-based approach is assessed on three iris datasets and the outcomes are compared with various state of the arts using three validation protocols in terms of equal error rate (EER). The comparative analysis perceived that the proposed method obtains a significant performance gain over the existing approaches and offers an improved benchmark for both, iris liveness detection and contact lens identification.

English

English

Post-mortem Iris Decomposition and its Dynamics in Morgue Conditions.

With increasing interest in employing iris biometrics as a forensic tool for identification by investigation authorities, there is a need for a thorough examination and understanding of postmortem decomposition processes that take place within the human eyeball, especially the iris. This can prove useful for fast and accurate matching of antemortem with postmortem data acquired at crime scenes or mass casualties, as well as for ensuring correct dispatching of bodies from the incident scene to a mortuary or funeral homes. Following these needs of forensic community, this paper offers an analysis of the coarse effects of eyeball decay done from a perspective of automatic iris recognition. We analyze postmortem iris images acquired for a subject with a very long postmortem observation time horizon (34 days), in both visible light and near-infrared light (860 nm), as the latter wavelength is used in commercial iris recognition systems. Conclusions and suggestions are provided that may aid forensic examiners in successfully utilizing iris patterns in postmortem identification of deceased subjects. Initial guidelines regarding the imaging process, types of illumination, and resolution are also given, together with expectations with respect to the iris features decomposition rates. Visible iris features possible for human, expert-based matching persists even up to 407 h postmortem, and near-infrared illumination is suggested for better mitigation of corneal opacity while imaging cadaver eyes (Post-mortem iris decomposition and its dynamics in morgue conditions. ArXiv pre-print, 2019).