Iris Recognition Research Articles

Iris Recognition System Using Advanced Segmentation Techniques and Fuzzy Clustering Methods for Robotic Control

The idea of developing a robot controlled by iris movement to assist physically disabled individuals is, indeed, innovative and has the potential to significantly improve their quality of life. This technology can empower individuals with limited mobility and enhance their ability to interact with their environment. Disability of movement has a huge impact on the lives of physically disabled people. Therefore, there is need to develop a robot that can be controlled using iris movement. The main idea of this work revolves around iris recognition from an eye image, specifically identifying the centroid of the iris. The centroid’s position is then utilized to issue commands to control the robot. This innovative approach leverages iris movement as a means of communication and control, offering a potential breakthrough in assisting individuals with physical disabilities. The proposed method aims to improve the precision and effectiveness of iris recognition by incorporating advanced segmentation techniques and fuzzy clustering methods. Fast gradient filters using a fuzzy inference system (FIS) are employed to separate the iris from its surroundings. Then, the bald eagle search (BES) algorithm is employed to locate and isolate the iris region. Subsequently, the fuzzy KNN algorithm is applied for the matching process. This combined methodology aims to improve the overall performance of iris recognition systems by leveraging advanced segmentation, search, and classification techniques. The results of the proposed model are validated using the true success rate (TSR) and compared to those of other existing models. These results highlight the effectiveness of the proposed method for the 400 tested images representing 40 people.

A novel approach to iris recognition at-a-distance: leveraging BW-CNN framework

A novel approach to iris recognition at-a-distance: leveraging BW-CNN framework

Iris biometric template identification and recognition scheme using a novel parallel fused encoder

Iris biometric template identification and recognition scheme using a novel parallel fused encoder

Tradeoffs between privacy and screening efficiency for air passengers – Implications for tourism supply chain management

ABSTRACT This study employs the stated preference method to explore airline passengers’ choices during security screening and examines their trade-offs between privacy and efficiency. We compare two Asian economic hub cities (i.e. Hong Kong and Shanghai), with 500 business and leisure travellers sampled from each city. A generalised mixed multinomial logit model is estimated. The results suggest that travellers display varied levels of privacy concerns, including biometrics, biographical data, identifiers, and behavioural information. The participants from Shanghai expressed a willingness to go through fingerprint scanning and to provide facial images. The travellers in Hong Kong displayed notable reluctance to receive iris scans and disclose political and religious affiliations, and they were particularly hesitant about sharing medication records. Despite some differences between the two markets, both groups strongly opposed smartphone tracking, including access to search history and GPS location. In general, business (vs. leisure) travellers, men (vs. women), individuals with higher education levels, and those with higher personal monthly incomes strongly preferred fast-track schemes that utilised personal data. Our findings highlight the possibility of making more effective transitions to high-tech screening methods that meet the changing expectations of travellers if total screening time can be significantly decreased.

Enhancing societal security: a multimodal deep learning approach for a public person identification and tracking system

In public spaces, threats to societal security are a major concern, and emerging technologies offer potential countermeasures. The proposed intelligent person identification system monitors and identifies individuals in public spaces using gait, face, and iris recognition. The system employs a multimodal approach for secure identification and utilises deep convolutional neural networks (DCNNs) that have been pretrained to predict individuals. For increased accuracy, the proposed system is implemented on a cloud server and integrated with citizen identification systems such as Aadhar/SSN. The performance of the system is determined by the rate of accuracy achieved when identifying individuals in a public space. The proposed multimodal secure identification system achieves a 94% accuracy rate, which is higher than that of existing public space person identification systems. Integration with citizen identification systems improves precision and provides immediate life-saving assistance to those in need. Utilising secure deep learning techniques for precise person identification, the proposed system offers a promising solution to security threats in public spaces. This research is necessary to investigate the efficacy and potential applications of the proposed system, including accident identification, theft identification, and intruder identification in public spaces.

HDN-Net: A Hybrid Deep Neural Network to Improve Iris Recognition in Unconstrained Environments with Eyeglasses

Recently, the use of iris recognition technology for biometric authentication has gained widespread acceptance due to the rich texture of the iris region, which provides a reliable standard for recognising individuals, as well as the non-intrusive nature of this method. However, the presence of eyeglasses poses a significant challenge to the accuracy of such systems. In unrestricted environments, current iris recognition techniques cannot effectively extract distinguishing features of the iris. Eyeglasses introduce scratches, specular reflections, dirt, blurriness, and other noise factors over the image of the iris, resulting in low recognition accuracy. To tackle this challenge, researchers have proposed the HDN-Net architecture. This architecture employs a multi-CNN model to combine the features of both the right and left iris images, extracting more distinguishing features to improve the accuracy of the classification task in the presence of challenges caused by eyeglasses. Experiment results show that the proposed iris recognition system achieves more promising performance compared to previous methods used in this field. The overall performance of our suggested HDN-Net method on the UBIRIS.V2 and CASIA-Iris.V4-1000 databases achieved 97.89% and 98.79% accuracy, respectively. Thus, the proposed HDN-Net method consistently outperforms other traditional and deep learning approaches and has the possibility to improve the accuracy of iris recognition systems (IRS) in real-world scenarios.

File Encryption by Iris Recognition System

File Encryption by Iris Recognition System

Deep learning techniques for hand vein biometrics: A comprehensive review

Biometric authentication has garnered significant attention as a secure and efficient method of identity verification. Among the various modalities, hand vein biometrics, including finger vein, palm vein, and dorsal hand vein recognition, offer unique advantages due to their high accuracy, low susceptibility to forgery, and non-intrusiveness. The vein patterns within the hand are highly complex and distinct for each individual, making them an ideal biometric identifier. Additionally, hand vein recognition is contactless, enhancing user convenience and hygiene compared to other modalities such as fingerprint or iris recognition. Furthermore, the veins are internally located, rendering them less susceptible to damage or alteration, thus enhancing the security and reliability of the biometric system. The combination of these factors makes hand vein biometrics a highly effective and secure method for identity verification.This review paper delves into the latest advancements in deep learning techniques applied to finger vein, palm vein, and dorsal hand vein recognition. It encompasses all essential fundamentals of hand vein biometrics, summarizes publicly available datasets, and discusses state-of-the-art metrics used for evaluating the three modes. Moreover, it provides a comprehensive overview of suggested approaches for finger, palm, dorsal, and multimodal vein techniques, offering insights into the best performance achieved, data augmentation techniques, and effective transfer learning methods, along with associated pretrained deep learning models. Additionally, the review addresses research challenges faced and outlines future directions and perspectives, encouraging researchers to enhance existing methods and propose innovative techniques.

Enhanced multimodal biometric access control system using chicken swarm optimization and self-organizing feature maps: a study on ear and iris recognition

Access control systems are crucial for securing sensitive data and system components by allowing authorized access while blocking unauthorized entities. Traditional unimodal biometric systems, make use of a single physiological or behavioral trait, have limitations such as susceptibility to spoofing and environmental constraints, leading to reduced reliability. This study explores an enhanced multimodal biometric access control system that combines ear and iris traits using an enhanced Self-Organizing Feature Map (SOFM) algorithm improved with Chicken Swarm Optimization (CSO). The system's performance is evaluated against traditional SOFM, with a focus on recognition accuracy and processing time. The data used to train the classifier for this study were collected from 190 individuals, encompassing a total of 2,280 images of iris, and ear traits. Preprocessing involved cropping, resizing, and grayscale conversion using histogram equalization. Feature extraction utilized Local Binary Patterns (LBP), followed by feature fusion at the feature level to create an integrated feature set. The enhanced SOFM algorithm was then applied for classification, with the CSO technique optimizing the learning rate and weight parameters for improved performance. At different thresholds, the CSO-SOFM classifier outperformed the standard SOFM classifier using metrics such as Sensitivity, Specificity, Precision, Accuracy and Recognition time.

A Study of New Indexing Techniques for Multimodal Identification Using Iris, Fingerprint, and Face Biometrics

This paper explores innovative indexing techniques for multimodal biometric identification systems, focusing on iris, fingerprint, and facial recognition technologies. With the proliferation of digital identity verification needs, traditional single-modal biometric systems often fall short in terms of accuracy, speed, and security. Multimodal biometric systems, which integrate multiple biological characteristics, are becoming crucial for enhancing identification performance. This study aims to present a comprehensive analysis of new indexing methods that can significantly improve the efficiency, accuracy, and security of multimodal biometric identification systems.

Extraction of Iris Characteristics Using a Combination Transform for Biometric Any individual Identification

The iris of the eye is used as an identifying and affirmative biometric in many vital applications such as banking and airports.And iris identification is one of the most reliable and accurate biometric identification systems available due to its stability and lack of sensitivity to longevity. In this article, authorized standard processes such as iris segmentation, normalization, and feature extraction were utilized to examine the iris of the eye. The circular hough transform is used for iris segmentation, while the daughman rubber-sheet model is used for iris picture normalization. Using a combination transform consisting of four consecutive transforms, feature encoding was employed to restore the most distinguishing aspects of irises. The 2-D fast fourier transform (FFT), radon transform, 1-D inverse fast fourier transform (IFFT), and 1-D discrete multi-wavelet transform are all examples of transforms. For pattern clustering, the kohonen self-organizing feature map (SOFM) was employe to the group that distinguished information vectors of the iris image features extracted using the combination transforms in order to visualize the close proximity of comparable irises features and scatter dissimilar ones. The matching process is carried out between test iris images and target iris photos of 224 people from the IITD database, with the similarity measured using the euclidian distance metric A part investigates the change of the parameters: FRR, FAR, TSR) of different collections of the PID and POD with the threshold levels (90:10,80:20,70:30,60:40,50:50) that resulted EER for the various thresholds are 0.070,0.070,0.080,0.090 and 0.095 respectively. The suggested approach yielded a TSR of 98% when compared to conventional methods.

Iris Identification Based on SVM-CNN Method

A person is automatically recognized in a biometric system by analyzing their distinct traits. Most people agree that iris recognition is the most accurate and dependable biometric identification method currently in use. The goal is to accurately and efficiently identify a person in real time by analyzing the sporadic patterns seen in the iris if an eye from some distance, by extracting strong features using deep learning technique. The extracting of significant features is important step that effect the overall accuracy of iris recognition system. In order to extract the iris characteristics, this research suggests a robust convolution neural network (CNN) structure. Then, an identity of the person is determine based on extracted features from his iris to support vector machine (SVM). The proposed system is examined on CASIA V. (1). Several parameters such as accuracy, precision, recall, and F-score are computed to evaluate the performance of the proposed system. The obtained result of the accuracy is about (99%). The proposed system results are compared with several previous methods and prove its effectiveness. A contemporary approach and the suggested method are contrasted and use deep learning for features extraction and the results depict our method outperforms other methods.

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.

A proposed biometric authentication hybrid approach using iris recognition for improving cloud security

Biometric systems have gained attention as a more secure alternative to traditional authentication methods. However, these systems are not without their technical limitations. This paper presents a hybrid approach that combines edge detection and segmentation techniques to enhance the security of cloud systems. The proposed method uses iris recognition as a biometric paradigm, taking advantage of the iris' unique patterns. We performed feature extraction and classification using hamming distance (HD) and convolutional neural networks (CNN). We validated the experimental findings using various datasets, such as MMU, IITD, and CASIA Iris Interval V4. We compared the proposed method's results to previous research, demonstrating recognition rates of 99.50 % on MMU using CNN, 97.18 % on IITD using CNN, and 95.07 % on CASIA using HD. These results indicate that the proposed method outperforms other classifiers used in previous research, showcasing its effectiveness in improving cloud security services.

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

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

A cancellable iris template protection scheme based on inverse merger and Bloom filter

Iris recognition has found extensive applications in real-world situations and financial contexts. However, Iris template protection schemes are highly vulnerable to well-planned attacks that can lead to the leakage of personal information. Once biological information is compromised, this loss is irreversible for the individual. Cancelable protection schemes for iris templates based on the Bloom filter have substantial attention in the field of iris biometrics. Nevertheless, Bloom filter-based template protection schemes face specific security challenges. Therefore, it is crucial to propose a method to protect iris templates that is both secure and efficient. To address irreversible limitations in security analysis, we propose a template protection scheme, a cancelable iris biometric protection scheme based on inverse merger and Bloom filter. The primary idea of the proposed scheme is to perform an inverse merger operation on the acquired codewords before mapping the iris templates to the Bloom filter specifically. Through a comparison of the sizes between the original templates and their inverted counterparts, the template with the smaller size is chosen as the definitive result, subsequently being mapped into the Bloom filter. Our proposed scheme exhibits significant advancements in accuracy across multiple datasets, as evidenced by empirical validations. In the optimal case, our model achieves an excellent performance of 98.04% in terms of GAR, while achieving a significant reduction of 0.51% in terms of EER. Furthermore, a comparative analysis with existing iris template protection methods is performed to evaluate its relative effectiveness in resisting the attack of averaging the columns of a block. The results demonstrate that the scheme exhibits robust resistance to such attacks. The experimental analysis demonstrated that the scheme provided a good balance between accuracy and safety.

Preprocessing of Iris Images for BSIF-Based Biometric Systems: Binary Detected Edges and Iris Unwrapping.

This work presents a novel approach to enhancing iris recognition systems through a two-module approach focusing on low-level image preprocessing techniques and advanced feature extraction. The primary contributions of this paper include: (i) the development of a robust preprocessing module utilizing the Canny algorithm for edge detection and the circle-based Hough transform for precise iris extraction, and (ii) the implementation of Binary Statistical Image Features (BSIF) with domain-specific filters trained on iris-specific data for improved biometric identification. By combining these advanced image preprocessing techniques, the proposed method addresses key challenges in iris recognition, such as occlusions, varying pigmentation, and textural diversity. Experimental results on the Human-inspired Domain-specific Binarized Image Features (HDBIF) Dataset, consisting of 1892 iris images, confirm the significant enhancements achieved. Moreover, this paper offers a comprehensive and reproducible research framework by providing source codes and access to the testing database through the Notre Dame University dataset website, thereby facilitating further application and study. Future research will focus on exploring adaptive algorithms and integrating machine learning techniques to improve performance across diverse and unpredictable real-world scenarios.

ENHANCE DOCUMENT VALIDATION UIPATH POWERED SIGNATURE VERIFICATION

Abstract—Signatures are widely used as a means of personal identification and verification. Many documents like bank cheques and legal transactions require signature verification. Signature-based verification of a large number of documents is a very difficult and time-consuming task. Consequently, an explosive growth has been observed in biometric personal verification and authentication systems that are connected with quantifiable physical unique characteristics (finger prints, hand geometry, face, ear, iris scan, or DNA) or behavioural features (gait, voice etc.). As traditional identity verification methods such as tokens, passwords, pins etc suffer from some fatal flaws and are incapable to satisfy the security necessities, the paper aims to consider a more reliable biometric feature, signature verification for the considering. We present a survey of signature verification systems. We classify and give an account of the various approaches that have been proposed for signature verification.

Depth-of-field extension method for iris recognition based on multi-wavelength illumination and dispersion principle

Depth-of-field extension method for iris recognition based on multi-wavelength illumination and dispersion principle

Effect of Particle Swarm Optimization Convolutional Neural Network in An Iris Recognition System

An iris recognition system based on Convolutional Neural Network with Particle Swarm Optimization (CNN-PSO) was developed to improve the identified hitches in the existing systems. Iris images of 150 and 108 persons were acquired from LAUIRIS (Nigeria) and CASIA (China) respectively. The images were resized and cropped after which Hough transform was used for effective localization of the iris region and normalised using Daugman’s rubber sheet model, while an efficient Cumulative Sum-based analysis method was used to extract discriminative features from the normalised iris images after which the iris code was generated. The iris code generated in a vector form was optimised with PSO after which they are fed into Convolutional neural network; the same procedure was engaged during enrolment and authentication to generate the iris template. Euclidean distance was used for decision making on test sample template and stored template. The system was implemented with MATLAB R2013a. The performance of the developed system was evaluated on LAURIS and CASIA, and compared with the existing systems (CNN, BPNN-PSO and BPNN) using False Acceptance Rate (FAR), False Rejection Rate (FRR) and Recognition Rate (RR). CNN-PSO has the highest recognition rate of 98.67% and 97.22% for LAUIRIS and CASIA respectively among the systems which showed an improvement over other three recognition technique. The developed CNN-PSO has not only produced an improved Iris recognition system over the others, with the highest recognition rate for both datasets but it also provides a significant recognition rate of black Iris images despite the limitations identified with black Iris images in separating Iris image from other part of the eyes. The developed