Iris Feature Research Articles

Fusion of Wavelet Features and Gabor Features for SVM-based Iris Verification

Iris verification now become increasingly prominent in biometric-based person verification systems. It has gained a significant role in biometric systems due to its stability, high uniqueness, contactless and non-invasive properties. Iris has more inherent distinctive features than other biometrics. Feature extraction of iris plays a crucial role in this system for accurate person verification. Using the feature extraction process, unique iris features like textural patterns, crypts, and furrows of iris are extracted. In our study, we did a fusion of Discrete Wavelet Transform (DWT) features with multiple wavelet bases (db4, haar, coif3, and sym4) and Gabor features, which contain a good amount of textural and localized information. Fusion here indicates the concatenation of the extracted features using the above techniques. In this work, we studied this method on the full iris only so that a maximum number of features can be extracted. This combined approach yielded a significant 112 number of features. The extracted features are then verified using a support vector machine (SVM) classifier based on radial basis function (RBF) kernel with training vs testing split ratios of 8:2, 6:4, 4:6 and 2:8. In this study, we achieved the highest overall verification accuracy of 95.9% with training vs testing split ratio of 8:2. For other training vs testing split ratios of 6:4, 4:6 and 2:8 we achieved overall verification accuracies of 91.4%, 93.2% and 91.2% respectively. We got an overall verification accuracy of 92.9%, considering training vs testing ratios of 8:2, 6:4, 4:6 and 2:8.

Morphologic features of iris in highly myopic eyes based on a novel swept-source optical coherence tomography.

To investigate the morphologic features of iris in the highly myopic (HM) eyes using a novel swept-source optical coherence tomography (SS-OCT). In this retrospective case-control study, 100 eyes of 100 patients scheduled to have cataract surgery were included, categorized into the control (22 mm< AL < 24.5 mm) and HM (AL ≥ 26 mm) groups. Iris volume (IV), area of anterior iris surface (IS), area of posterior IS, and average iris thickness (IT), as well as anterior chamber volume (ACV) and trabecular-iris space at 500 µm (TISA 500) were evaluated using SS-OCT. The associated factors with morphologic features of iris were also investigated. The HM group showed significantly larger IV and area of anterior and posterior IS than the control group (all P < 0.001), while no difference was identified in IT between the groups. Similar trend in IV was seen in the superior and nasal segments, and area of anterior and posterior IS showed similar trends in all segments except the temporal segment. The IV, area of anterior and posterior IS were all positively correlated with AL (all P < 0.001). Multivariate linear regression revealed that a larger IV was associated with greater ACV. Both larger areas of anterior IS and posterior IS were associated with male, longer AL, greater ACV, and smaller TISA 500. The HM eyes showed larger IV and area of IS than the control eyes, indicating a coronal expansion of the iris with AL. Iris morphology correlated with anterior chamber angle configuration.

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.

Gait recognition for farm workers via multi-scale temporal feature perception

The monitoring and management of farm workers play a crucial role in agricultural production. The real-time monitoring of farm workers can effectively identify them, facilitating standardized management and operation of the farm. Gait, as a unique biometric feature that can be recognized from a distance, is more suitable for identifying individual farm workers walking on the farm at a distance compared to facial and iris features. Despite the increasing research on gait recognition techniques, existing gait recognition models are still lacking in temporal modeling. The gait of farm workers walking through farmland is greatly influenced by the environment, making it crucial to model the temporal information of gait. In this study, we captured videos of 50 farm workers from 11 different perspectives walking in real agricultural fields and formed the Farm Worker Gait Dataset (FWGD). In comparison with existing gait recognition models, we present a multi-scale temporal feature modeling method based on the GaitBase model and obtain a performance improvement on the FWGD dataset. Specifically, we propose an Adaptive Temporal Self-Attention (ATSA) mechanism to extract the most representative features based on temporal contextual temporal relations. Additionally, we utilize a dilated convolutional layer to extract discrete temporal features and employ a Multi-scale Temporal Perception Module (MSTP) with three branches to perceive and aggregate temporal features. With the assistance of MSTP and ATSA, our model significantly improves the discriminability of temporal features, thus enhancing the performance of farm worker gait recognition. Extensive experiments demonstrated that our proposed method outperformed state-of-the-art gait recognition methods on the FWGD dataset.

Feature Level Fusion of Fingerprints and IRIS for the Enhancement of Biometric System

Background:: With the growing need for information safety and security rules in every corner of the globe. The biometric innovation has been used widely all over the world in daily life. In this view, the Multimodal-based biometric technique has gained popularity and interest due to its capability to resolve various problems associated with single-model biometric identification systems Methods:: In this research, an advanced multimodal-based biometric recognition system is introduced, which depends on multilayer perceptron (MLP), and it identifies humans using biometric features of Fingerprints and IRIS. To build an efficient model, the popular model called Rest- Net50 was used, the gradient method was used and the hinge technique was used as a loss function. For the technique of fusion, different techniques are used to show the effect of fusion in our model. The recognition rate of introduced systems was assessed by experimenting with various techniques on SDUMLA-HMT datasets based on the multimodal biometric datasets. The recognition rate showed that merging two biometric features in biometric recognition systems gives the best results compared to a single biometric feature. Results:: The results obtained showed the superiority of our model with other existing techniques by obtaining a recognition rate of 99.79% with a feature-based fusion method. Conclusion:: The introduced system used the MLP deep learning technique. The feature level fusion was applied to recognize the user using IRIS and fingerprints. To the best of our belief, this is the first research to incorporate MLP and multimodal-based biometric systems using IRIS and fingerprints.

Non-invasive coronary artery disease identification through the iris and bio-demographic health profile features using stacking learning

This study proposes a non-invasive method for predicting Coronary Artery Disease (CAD) using iris analysis, patient data, and Machine Learning (ML), primarily with iris images. It involved 281 participants, comprising 155 CAD patients and 126 non-patient controls, with eye images and biodemographic data collected at a Cardiology outpatient clinic. The study explored three scenarios: Scenario-I focused on biodemographic data, Scenario-II on iris features, and Scenario-III combined iris images and data. Iris processing included location determination, normalization, and heart region selection, with image enhancement via adaptive histogram equalization. Feature extraction through a 2-level wavelet transform generated 272 attributes, including statistical, Gray Level Co-occurrence Matrix, and Gray Level Run Length Matrix features for eight subcomponents. Correlation-based selection identified the best features, and classification employed ML techniques and incorporated stacking learning to enhance the results. Scenario-I achieved the highest accuracy at 83.57% among all evaluated algorithms. In Scenario-II, the proposed algorithm consistently outperformed others, achieving 94.88% accuracy and strong performance in other metrics, highlighting its effectiveness. In Scenario-III, the algorithm maintained superiority with 96.07% accuracy, specificity, recall, and area under the curve values. The proposed algorithm consistently outperforms other methods across scenarios, indicating its potential for CAD diagnosis, making it a promising choice for future CAD systems. The proposed algorithm presents a novel approach to the preliminary diagnosis of CAD, eliminating the necessity for electrocardiography, echocardiography, or effort tests. It also enables seamless integration into telemedicine systems, allowing for tele-diagnosis to conduct preliminary assessments before routine clinical practice.

Iris-Based Biometric Identification Using a Combination of the Right - Left Iris Statistical Features

A combination between the information extracted for both right iris and left iris could increase the efficacy of the biometric recognition systems. In this paper, we propose a biometric identification method based on density of image patterns extracted from human iris images and the combination and comparison of the right iris and the left iris characteristics. The density of the patters approach for processed images can be a new biometric feature used to implement a biometric recognition system with high performance when a small feature dimension is used. In this way, we can maximize the retention of the effective biometric information. The experiments were conducted on the MMU Iris Database containing 225 images of the left eye and 225 images of the right eye. Two morphological Top-hat and Hit or Miss transforms were implemented to find out the particular pattern of pixels. They allow for the enhancement of detail in images. Then, a statistical feature extraction technique is employed to derive the density of the patterns in morphological transformed images. To assess the density of the patterns differences between the right and left iris data groups, the Pearson’s correlation coefficient (PCC) is computed. We reported very good results with a PCC of 0.6164 (strong and positive correlation) for Top-hat morphological operation whilst the Hit or Miss transform returns a PCC of 0.0127 so there is no relationship between the density of the patterns in the right and left irises.

Biometric Identification Advances: Unimodal to Multimodal Fusion of Face, Palm, and Iris Features

Biometric Identification Advances: Unimodal to Multimodal Fusion of Face, Palm, and Iris Features

Exploring Phenotypic Diversity of Human Iris Features and Skin Color in Punjabi and Khyber Pakhtunkhwa Population of Pakistan

Exploring Phenotypic Diversity of Human Iris Features and Skin Color in Punjabi and Khyber Pakhtunkhwa Population of Pakistan

A Multimodal Biometric System for Iris and Face Traits Based on Hybrid Approaches and Score Level Fusion

The increasing need for information security on a worldwide scale has led to the widespread adoption of appropriate rules. Multimodal biometric systems have become an effective way to increase recognition precision, strengthen security guarantees, and reduce the drawbacks of unimodal biometric systems. These systems combine several biometric characteristics and sources by using fusion methods. Through score-level fusion, this work integrates facial and iris recognition techniques to present a multimodal biometric recognition methodology. The Histogram of Oriented Gradients (HOG) descriptor is used in the facial recognition system to extract facial characteristics, while the deep Wavelet Scattering Transform Network (WSTN) is applied in the iris recognition system to extract iris features. Then, for customized recognition classification, the feature vectors from every facial and iris recognition system are fed into a multiclass logistic regression. These systems provide scores, which are then combined via score-level fusion to maximize the efficiency of the human recognition process. The realistic multimodal database known as (MULB) is used to assess the suggested system's performance. The suggested technique exhibits improved performance across several measures, such as precision, recall, accuracy, equal error rate, false acceptance rate, and false rejection rate, as demonstrated by the experimental findings. The face and iris biometric systems have individual accuracy rates of 96.45% and 95.31% respectively. The equal error rates for the face and iris are 1.79% and 2.36% respectively. Simultaneously, the proposed multimodal biometric system attains a markedly enhanced accuracy rate of 100% and an equal error rate as little as 0.26%.

An effective iris biometric privacy protection scheme with renewability

Iris is considered a unique, robust biometric that can be used as an authentication factor. Privacy protection of individuals’ iris templates is an essential requirement. The essential attributes needed to ensure privacy of biometrics are irreversibility, renewability, and unlinkability. Information distortion is an effective way to achieve the privacy. However, this may lead to some degradation of recognition performance. A trade-off between recognition performance and the privacy level provided by the authentication system arises when it tries to provide both simultaneously. This paper proposes a novel approach that ensures the privacy of each individual's iris by distorting iris feature information. At the meantime, this approach balances the trade-off mentioned above up to a tolerable level. The binary feature templates of iris biometrics are transformed into another irreversible binary representation by manipulating them block-wise. The results obtained against two datasets using proposed approach illustrate excessive Genuine Acceptance Rates (GAR) when the approach accepts imposters at a rate which is reduced to 0.01 %. The proposed approach satisfies the three attributes that a biometric system should fulfil, namely irreversibility, renewability, and unlinkability. The renewability attribute induces the cancellability of iris biometric template. Compared to the existing works, the proposed approach maintains Equal Error Rate (EER) at 0.18 %.

Iris Recognition Approach for Preserving Privacy in Cloud Computing

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

Morphobiochemical characteristics of iris (Iridaceae) species and cultivars during adaptation in the forest-steppe of the Ob region

Morphobiochemical characteristics of iris (Iridaceae) species and cultivars during adaptation in the forest-steppe of the Ob region

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.

English

A multi modality biometric authentication system can combine information from various modalities and provides accurate results compared to biometric systems used individually. A novel ensemble classifier-based multimodal biometric authentication system has been proposed in this work. The performance of the proposed multimodal authentication system is measured using parameters such as Accuracy, Sensitivity and Specificity and compared with the SVM classifier, Decision tree classifier when fingerprint, Iris, and Face features are used. The results of the multimodal biometric system are also compared with the biometric authentication system when fingerprint features are used and combined with Fingerprint & Iris features. The proposed ensemble classifier-based multimodal biometric authentication system provides an accuracy of 96.75%, Sensitivity of 94.74%, Specificity of 98.95%, FAR of 1.04 and FRR of 5.26. The proposed ensemble classifier outperforms SVM and decision tree classifiers regarding performance measures.

Personal Authentication System Based Iris Recognition withDigital Signature Technology

Authentication based on biometrics isused to prevent physical access to high-security institutions. Due to the rapid rise of information system technologies, Biometrics is now being used in applications for accessing databases and commercial workflowsystems. These applications need to implement measures to counter security threats. Many developers are exploring and developing novel authentication techniques to prevent these attacks. However, the most challenging problem is keeping biometric data while maintaining the functional performance ofidentity verification systems. This paper presents a biometrics-based personal authentication system combining a smart card, a Public Key Infrastructure (PKI), and iris verification technologies. Raspberry Pi 4 Model B+ is the core of hardware componentswith an IR Camera. Following that idea, we designed an optimal image processing algorithm in OpenCV/ Python, Keras, and sci-kit learn libraries for feature extraction and recognition chosen for application development in this project. The implemented systemgives an accuracy of (97% and 100%) for the left and right (NTU) iris datasets, respectively, after training. Later, the person verification based on the iris feature is performed to verify the claimed identity and examine the system authentication. Thetime of essential generation, Signature, and Verification is 5.17sec,0.288, and 0.056 for the NTU iris dataset. This work offers a realistic architecture to implement identity-based cryptography with biometrics using the RSA algorithm.

Laser Iridotomy for Pupillary Block in Children with Endogenous Uveitis

Objective. Development of a differential approach to laser iridotomy in children with endogenous uveitis considering iris characteristics and the reaction of children’s eyes to different laser types.Patients and methods. Thirty-six laser iridotomies were conducted in 36 children aged 8–16 years with pupillary block. Nidek YAG laser was used in YAG (1064 nm) and SLT (532 nm) modes.Results. The frequency of hemorrhagic complications during surgery decreased to 19.4 % since 2009. The frequency of exudative reactions and proliferative syndrome with closure of laser coloboms in 1-6 months after surgery decreased to 33.3 % (25.7 % and 40.9 % respectively).Conclusion. We recommended the patented differential approach for effective nontraumatic laser iridotomy in children with endogenous uveitis. In a thin iris single-stage YAG laser perforation should be performed in maximum bombe and multiple perforation in cases of multi-chamber bombe. In a dense thick iris or in cases of former iridotomy closure two-stage approach should be performed. First, circular coagulation should be done in the form of a ring or spot at the site of the planned coloboma. Second, YAG-laser perforation should be done in 10–14 days. The iris vessels topography, reflexogenic zones, and the peculiarities of the iris reactions to the lasers in children with endogenous uveitis should be considered when choosing a place for a coloboma. The use of thermal coagulating lasers should be limited, especially on thin irises. Active anti-inflammatory therapy and eye monitoring are necessary after laser surgery. The differential approach is safe, effective, and can be recommended for implementation in the practice of pediatric ophthalmology.

FIMBISAE: A Multimodal Biometric Secured Data Access Framework for Internet of Medical Things Ecosystem

Information from the Internet of Medical Things (IoMT) domain demands building safeguards against illegitimate access and identification. Existing user identification schemes suffer from challenges in detecting impersonation attacks which leave systems vulnerable and susceptible to misuse. Significant advancement has been achieved in the domain of biometrics and health informatics. This can take a step ahead with the usage of multimodal biometrics for the identification of healthcare system users. With this aim, the proposed work explores the fingerprint and iris modality to develop a multimodal biometric data identification and access control system for the healthcare ecosystem. In the proposed approach, minutiae-based fingerprint features and a combination of local and global iris features are considered for identification. Further, an index space based on the dimension of the feature vector is created, which gives a 1-D embedding of the high-dimensional feature set. Next, to minimize the impact of false rejection, the approach considers the possible deviation in each element of the feature vector and then stores the data in possible locations using the predefined threshold. Besides, to reduce the false acceptance rate, linking of the modalities has been done for every individual data. The modality linking thus helps in carrying out an efficient search of the queried data, thereby minimizing the false acceptance and rejection rate. Experiments on a chimeric iris and fingerprint bimodal database resulted in an average of 95% reduction in the search space at a hit rate of 98%. The results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification.

A Novel Visible Iris Recognition using Color Monogenic Wavelets

Visible light (VL) iris recognition technology[1] has a growing number of applications in the field of security and citizen identification with the growing number of surveillance cameras and smartphone cameras that acquire iris images in the visible wavelength range. The problem of iris recognition has also led to a growing interest in the development of robust algorithms for visible light iris recognition. Visible light iris recognition is also important for better results on light-colored irises which observe a richer capturing of iris patterns in the visible light range as compared to the Near-Infrared (NIR) range.VL iris images have been shown to have more noise in the form of shadows and reflections and are not as efficient as NIR iris images in capturing iris patterns for dark-colored irises. In this research work, a novel color monogenic wavelength based VL iris feature extraction is performed on widely used VL databases (PolyU[2] and UTIRIS[3]). The results of the proposed algorithm outperform the commonly used algorithms for NIR iris recognition