Iris Data Research Articles

Generalized ATOVIC System Based on Triangular Fuzzy Numbers for Pattern Classification

Diseases such as diabete and cancer must be detected and treated precociouse. They are generally detected using recognition and classification systems. The latter must be choosen according their accuracies and other measures. In addition, some difficulties and ambiguities can exist related with features representing data sets such as their variant types (eg., integer, nominal, etc.) or overlapping between them. Also, data can be imprecise or incomplete and can influence negatively classification results. Fuzzy sets and their generalizations, resolve problems of data uncertainty by modeling them with linguistic variables. The latter are presented with membership functions having different shapes. Triangular fuzzy numbers which represent tringular functions are very rarely used in classification systems. The cause can be that triangular shape does not preserve in case of some calculations. This research proposes to generalize the classification system "‘Amended Fused TOPSIS-VIKOR for classification"’ (ATOVIC) with triangular fuzzy numbers and to apply it to some UCI date sets. The objective is to unveil the impact of triangular fuzzy numbers in classification. The results of generalized fuzzy ATOVIC are compared to those of its crisp version and those of some existing classification systems using fuzzy sets. Results of ATOVIC generalized with tringular fuzzy numbers are promising. Especially its results on breast cancer Wisconsin and IRIS data sets are higher than Mamdani and Segano fuzzy inference systems using triangular fuzzy numbers from literature.

Fisher’s pioneering work on discriminant analysis and its impact on Artificial Intelligence

Sir Ronald Aylmer Fisher opened many new areas in Multivariate Analysis, and the one which we will consider is discriminant analysis. Several papers by Fisher and others followed from his seminal paper in 1936 where he coined the name discrimination function. Historically, his four papers on discriminant analysis during 1936–1940 connect to the contemporaneous pioneering work of Hotelling and Mahalanobis. We revisit the famous iris data which Fisher used in his 1936 paper and in particular, test the hypothesis of multivariate normality for the data which he assumed. Fisher constructed his genetic discriminant motivated by this application and we provide a deeper insight into this construction; however, this construction has not been well understood as far as we know. We also indicate how the subject has developed along with the computer revolution, noting newer methods to carry out discriminant analysis, such as kernel classifiers, classification trees, support vector machines, neural networks, and deep learning. Overall, with computational power, the whole subject of Multivariate Analysis has changed its emphasis but the impact of this Fisher’s pioneering work continues as an integral part of supervised learning in Artificial Intelligence (AI).

A new distance measure between two basic probability assignments based on penalty coefficient

Uncertainty information processing is a challenging topic in information science. Dempster-Shafer evidence theory shows excellent performance when dealing with uncertainty in information representation, fusion, and processing. The measurement of the distance between the basic probability assignments (BPA) in Dempster-Shafer evidence theory is a powerful tool that deals with uncertainty in evidential information processing. Most of the existing methods are based on the difference in the assessment of evidential degree for different hypotheses, with a notable example Belief-Jensen-Shannon (BJS) divergence. However, these methods ignore the information that is carried by different units in the power sets that represent the hypotheses, i.e., the difference on the hypotheses themselves that include in the evidential degree assessment of different BPA should be considered in the distance measurement. Therefore, in this paper, a novel method grounded in BJS divergence and incorporating a penalty coefficient to rectify the distance between basic probability assignments is proposed. This methodology showcases commendable properties, including symmetry, boundedness, and adherence to the triangle inequality. To demonstrate the efficiency of the proposed distance measurement method between BPAs, we apply it to two common-used machine learning data sets: Iris data set and Dry Beans data set. Compared to the represented existing evidence distance measurement approaches, the proposed method yields superior performance in both data sets with respect to classification accuracy.

Educational Practice Based on the Integration of Support Vector Machines and English Curricula

At present, IT (Information Technology) with computer network as its core has begun to integrate into CET (College English Teaching) practice, which has brought unprecedented influence on ELT (English Language Teaching). This paper adopts the strategy of active learning to build SVM, overcomes the shortcomings of typical SVM, and comprehensively considers various actual situations to build an assessment model of college English classroom instructional quality. The experimental results show that the classification accuracy of this algorithm can reach 96.21% on Zoo data set and 94.36% on Iris data set. This study is of great theoretical and practical value for improving the level of college English classroom teaching in the IT ecological environment.

On the security risk of pre-image attack on cancelable biometrics

Over the years, a number of biometric template protection schemes, often based on the notion of “cancelable biometrics”, have been proposed. An ideal cancelable biometric algorithm complies with four criteria: irreversibility, revocability, unlinkability, and performance preservation. Cancelable biometrics employs an irreversible but distance preserving transformation to convert the original biometric templates into protected templates. Matching in the transform domain can be accomplished due to the property of distance preservation. However, distance preservation also entails security issues, a point often overlooked in existing research. In this paper, we have conducted a comprehensive security analysis of distance preservation in cancelable biometrics for the first time. The analysis is based on a pre-image attack, which is launched to break the security of cancelable biometrics under Kerckhoffs’s assumption. Furthermore, we propose a general security analysis framework under the single and cross-transformation attacks, which also employs an information leakage estimation strategy based on mutual information as a complement. The experimental results performed on real face, iris, and fingerprint data demonstrate that the risks originating from the matching scores computed from the distance/similarity of two cancelable templates greatly compromise the security of cancelable biometric schemes, including the classic Biohashing, Index-of-max hashing, Non-linear multi-dimensional spectral hashing, Indexing-First-One hashing, Bloom Filter and Two-factor Protected Minutia Cylinder-Code. The security versus accuracy trade-off is discussed and recommendations for designing a biometric system secure against pre-image attacks are also proposed. The source code is available at github.com/biometricsecurity/CBrisks.

A standard ranking algorithm for robust iris template protection

In iris biometric recognition systems, protecting the storage and transmission of iris templates is crucial, and template protection techniques are pivotal for ensuring their security. A prevalent approach involves using indexing methods as an effective algorithm for iris template protection, leveraging the index or rank of the extracted iris code to generate a secure iris template. Meantime, many privacy threats to biometric data have emerged, necessitating heightened protection measures. Specifically, protecting the privacy of iris data is imperative within the context of iris template protection during recognition processes. As stipulated by the international standard ISO/IEC 30136, effective iris template protection must concurrently meet the criteria of irreversibility, revocability, and unlinkability. Nevertheless, existing indexing methods on iris template protection faced the formidable challenge of simultaneously fulfilling these three privacy requirements while maintaining the efficacy of iris recognition. This paper introduces a standard ranking (standardR) algorithm, named standardR, designed to enhance the security of iris templates by transforming each iris template into an irreversible representation. The experimental results on the benchmarked Casia-Iris-interval dataset, along with two additional iris datasets MMU1 and UBRIS 1, demonstrate the efficacy of the proposed algorithm. The proposed standardR algorithm achieves an equal error rate (EER) of 0.1695% and an area under the curve of 0.93011% with the Casia-Iris-Interval dataset. Furthermore, the algorithm maintains efficient recognition with a reduced iris code length of 1280 bits, a time complexity of O(n log n), and satisfies the biometric template protection (BTP) requirements in irreversibility, unlinkability, and renewability.

Uniqueness of Iris Pattern Based on the Auto-Regressive Model.

In this paper, we evaluate the uniqueness of a hypothetical iris recognition system that relies upon a nonlinear mapping of iris data into a space of Gaussian codewords with independent components. Given the new data representation, we develop and apply a sphere packing bound for Gaussian codewords and a bound similar to Daugman's to characterize the maximum iris population as a function of the relative entropy between Gaussian codewords of distinct iris classes. As a potential theoretical approach leading toward the realization of the hypothetical mapping, we work with the auto-regressive model fitted into iris data, after some data manipulation and preprocessing. The distance between a pair of codewords is measured in terms of the relative entropy (log-likelihood ratio statistic is an alternative) between distributions of codewords, which is also interpreted as a measure of iris quality. The new approach to iris uniqueness is illustrated using two toy examples involving two small datasets of iris images. For both datasets, the maximum sustainable population is presented as a function of image quality expressed in terms of relative entropy. Although the auto-regressive model may not be the best model for iris data, it lays the theoretical framework for the development of a high-performance iris recognition system utilizing a nonlinear mapping from the space of iris data to the space of Gaussian codewords with independent components.

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.

Reliable human authentication using AI-based multibiometric image sensor fusion: Assessment of performance in information security

Reliable human authentication is essential for assuring the availability, confidentiality, and integrity of sensitive data and resources in the field of information security. Due to the vulnerabilities of conventional authentication techniques like passwords and PINs, interest in biometric-based authentication systems has developed. To get over this, we suggest an efficient voice and iris recognition-based multimodal biometric verification strategy for human authenticating tools. We first collect the voice and iris datasets from 150 people. Then, we used a median filter and high pass filter to preprocess the voice and iris data. The features of voice identification are extracted using the shifted delta cepstral coefficient (SDCC) and the Mel frequency discrete wavelet coefficient (MFCC), and these two coefficients are evaluated. The outcomes of the extraction of iris identification features using Local binary pattern (LBP) and Speeded robust features (SURF) are evaluated. The classifier Fine Tuned Cuckoo Search Optimized Convolutional Neural Network (FCSO-CNN) performs voice and iris recognition modalities. Voice and iris biometric systems may be combined into a single multimodal biometric system by fusing their respective feature sets and scoring algorithms. The results of the computer simulation demonstrate that for speech recognition, using the SDC and MFC coefficients produces better results, while for iris recognition, using the LBP and FCSO-CNN experiment produces superior outcomes. Additionally, the scores fusion works superior to other scenarios in the proposed multimodal biometrics system. The suggested system provides a dependable and strong solution for human identification, assisting in the improvement of security measures across a variety of industries, including financial institutions, governmental agencies, and the defense of key infrastructure.

Synthetic Iris Images: A Comparative Analysis between Cartesian and Polar Representation.

In recent years, the advancement of generative techniques, particularly generative adversarial networks (GANs), has opened new possibilities for generating synthetic biometric data from different modalities, including-among others-images of irises, fingerprints, or faces in different representations. This study presents the process of generating synthetic images of human irises, using the recent StyleGAN3 model. The novelty presented in this work consists in producing generated content in both Cartesian and polar coordinate representations, typically used in iris recognition pipelines, such as the foundational work proposed by John Daugman, but hitherto not used in generative AI experiments. The main objective of this study was to conduct a qualitative analysis of the synthetic samples and evaluate the iris texture density and suitability for meaningful feature extraction. During this study, a total of 1327 unique irises were generated, and experimental results carried out using the well-known OSIRIS open-source iris recognition software and the equivalent software, wordlcoin-openiris, newly published at the end of 2023 to prove that (1) no "identity leak" from the training set was observed, and (2) the generated irises had enough unique textural information to be successfully differentiated between both themselves and between them and real, authentic iris samples. The results of our research demonstrate the promising potential of synthetic iris data generation as a valuable tool for augmenting training datasets and improving the overall performance of iris recognition systems. By exploring the synthetic data in both Cartesian and polar representations, we aim to understand the benefits and limitations of each approach and their implications for biometric applications. The findings suggest that synthetic iris data can significantly contribute to the advancement of iris recognition technology, enhancing its accuracy and robustness in real-world scenarios by greatly augmenting the possibilities to gather large and diversified training datasets.

ENSEMBLE RESAMPLING SUPPORT VECTOR MACHINE, MULTINOMIAL REGRESSION TO MULTICLASS IMBALANCED DATA

Imbalanced data is a commonly encountered issue in classification analysis. This issue gives rise to prediction errors in the classification process, which in turn affects the sensitivity, particularly in the minority class. Resampling techniques can be employed as a means to mitigate the issue of Imbalanced data. Furthermore, ensemble approaches are Utilized in the classification procedure to augment the performance of classification. The present study assesses the efficacy of the bagging ensemble approach in conjunction with ADASYN as a means of addressing the aforementioned issue. The dataset Utilized in this work comprises Imbalanced Glass Identification data, Imbalanced Iris data, and Imbalanced synthetic data. The study Centres on the Utilization of Support Vector Machines (SVM) with parameter optimization using repeated cross-validation (k = 10) and the application of multinomial regression. The evaluation of classification outcomes involves a comparison between the ensemble technique and multinomial regression. This comparison is conducted under pre- and post-resampling conditions, with the evaluation metrics being accuracy, sensitivity, and specificity. The analysis of classification outcomes across the three datasets suggests that the ensemble resampling SVM approach and multinomial regression exhibit superior performance compared to the ensemble SVM and multinomial regression approaches when applied to non-resampled data. Resampling of data has been observed to enhance sensitivity, particularly in the minority class.

Deep learning based biometric authentication using electrocardiogram and iris

Authentication systems play an important role in wide range of applications. The traditional token certificate and password-based authentication systems are now replaced by biometric authentication systems. Generally, these authentication systems are based on the data obtained from face, iris, electrocardiogram (ECG), fingerprint and palm print. But these types of models are unimodal authentication, which suffer from accuracy and reliability issues. In this regard, multimodal biometric authentication systems have gained huge attention to develop the robust authentication systems. Moreover, the current development in deep learning schemes have proliferated to develop more robust architecture to overcome the issues of tradition machine learning based authentication systems. In this work, we have adopted ECG and iris data and trained the obtained features with the help of hybrid convolutional neural network- long short-term memory (CNN-LSTM) model. In ECG, R peak detection is considered as an important aspect for feature extraction and morphological features are extracted. Similarly, gabor-wavelet, gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM) and principal component analysis (PCA) based feature extraction methods are applied on iris data. The final feature vector is obtained from MIT-BIH and IIT Delhi Iris dataset which is trained and tested by using CNN-LSTM. The experimental analysis shows that the proposed approach achieves average accuracy, precision, and F1-core as 0.985, 0.962 and 0.975, respectively.

SecureSense: Enhancing Person Verification through Multimodal Biometrics for Robust Authentication

Biometrics provide enhanced security and convenience compared to conventional methods of individual authentication. A more robust and effective method of individual authentication has emerged due to recent advancements in multimodal biometrics. Unimodal systems offer lower security and lack the robustness found in multimodal biometric systems. The research paper introduces a novel approach, employing multiple biometric modalities, including face, fingerprint, and iris, to authenticate users in a multimodal biometric system. The paper proposes the ”Secure Sense” framework, which combines multiple biometric modalities to improve person verification accuracy. The proposed system utilizes both web-based and real-time datasets. For the web-based dataset, we employed the Chicago Face dataset for facial data, the MMU1 dataset for iris data, and the SOCOfing dataset for fingerprint data. In real-time data collection, facial data is captured using a Zebronics Zeb-Gem webcam, fingerprint data is obtained using the Mantra MFS scanner, and iris data is collected using the Mantra MIS scanner. In the envisioned system, we introduce an innovative approach that employs a decision-level fusion technique across three unique biometric modalities, resulting in an impressive accuracy rate of approximately 93% across all modalities.

A Novel Training Approach in Deep Spiking Neural Network Based on Fuzzy Weighting and Meta-heuristic Algorithm

The challenge of supervised learning in spiking neural networks (SNNs) for digit classification from speech signals is examined in this study. Meta-heuristic algorithms and a fuzzy logic framework are used to train SNNs. Using gray wolf optimization (GWO), the features obtained from audio signals are reduced depending on the dispersion of each feature. Then, it combines fuzzy weighting system (FWS) and spike time-dependent flexibility (STDP) approach to implement the learning rule in SNN. The FWS rule produces a uniformly distributed random weight in the STDP flexibility window, so that the system requires fewer training parameters. Finally, these neurons are fed data to estimate the training weights and threshold values of the neurons using wild horse algorithm (WHO). With the parameters given, these rule weights are applied to appropriately display the class's share in extracting the relevant feature. The suggested network can classify speech signals into categories with 97.17% accuracy. The dataset was obtained using neurons operating at sparse biological rates below 600 Hz in the TIDIGITS test database. The suggested method has been evaluated on the IRIS and Trip Data datasets, where the classification results showed a 98.93% and 97.36% efficiency, respectively. Compared to earlier efforts, this study's results demonstrate that the strategy is both computationally simpler and more accurate. The accuracy of classification of digits, IRIS and Trip Data has increased by 4.9, 3.46 and 1.24%, respectively. The principal goal of this research is to improve the accuracy of SNN by developing a new high-precision training method.

Analysis of Naive Bayesian and Back Propagation algorithms in iris classification

An efficient taxonomy of irises can provide botanists with valuable tools. Machine learning algorithms can effectively improve the performance of iris classification models because they can automatically analyze and summarize data. To this end, this paper introduces Naive Bayesian (NB) and Back Propagation (BP) to build classification models. When creating the NB model, the petal and sepal data from the iris dataset are used sequentially as classification criteria to classify the data. When constructing the BP model, the author sets different iterations and outputs the loss function and accuracy of the BP model under different iterations. The study finds that the NB model has higher classification accuracy when using petal length and petal width as classification criteria, which is 17% higher than the classification accuracy using sepal length and sepal width. Therefore, the NB model is more suitable for classifying independent data. By studying the use of the BP algorithm to classify iris flowers, the automatic classification of iris flowers can be realized and the accuracy of classification can be improved. Compared with the traditional NB algorithm, the BP algorithm can better mine the hidden patterns and information in the iris data and make effective classifications. This study provides new insights and discoveries for the taxonomic study of Iris plants.

Toroidal mantle flow beneath the NE termination of the Kuril–Kamchatka subduction zone from seismic anisotropy

SUMMARY This study presents the findings of a splitting analysis conducted on core-refracted teleseismic shear waves (SKS, SKKS and PKS, called together as XKS) and local shear waves, obtained from a dense seismological network spanning the Kamchatka Peninsula. The objective of the study is to examine the pattern of mantle flow beneath the study area through the investigation of seismic anisotropy. The peninsula is situated at the northeastern end of the Kuril–Kamchatka subduction zone, where the Kuril trench intersects with the western boundary of the Aleutian trench. The data set utilized in this study comprises waveform data from a dense network of seismic stations (99 broad-band and short-period stations for the local shear wave splitting analysis and 69 broad-band stations for the SKS splitting analysis). The seismograms were downloaded from publicly available data repositories including the IRIS Data Management Center and the GFZ Data Services (GEOFON program). The dense station coverage allows us to investigate the lateral variations in anisotropy, providing insights into the flow patterns within the mantle. The processing of the combined data sets of local shear wave and teleseismic XKS waves allowed us to partially decipher the source of anisotropy in the mantle. Small delay (splitting) times (∼0.35 s) observed from the local-S data suggest that anisotropy in the mantle wedge is relatively weak with lateral variations. Larger splitting times (∼1.1 s) observed for the XKS waves relative to local S suggest that the main part of splitting on the XKS waves occurs in the subslab mantle. On the other hand, the rotational pattern of seismic anisotropy observed by both the local S and XKS waves suggests the presence of a toroidal flow at the NE edge of the subducting slab, which affects both the mantle wedge and subslab mantle. For the regions away from the edge of the slab, the mantle flow seems to be governed mainly by the drag of the lithospheric plate over the underlying asthenosphere.

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.

ИССЛЕДОВАНИЕ МЕТОДОВ И АЛГОРИТМОВ РЕДУКЦИИ НЕЧЕТКИХ ПРАВИЛ В МОДЕЛЯХ ОЦЕНКИ ДИСКРЕТНОГО СОСТОЯНИЯ ОБЪЕКТОВ

The article describes the stages and results of the study of methods and genetic algorithms used to reduce fuzzy rules in models for assessing the discrete state of objects. Such models are fuzzy knowledge bases and fuzzy logical inference algorithms. A study was conducted of the influence of the parameter values of genetic algorithms on the results of reduction of the initial and intermediate knowledge bases (on the reduction of the number of rules in the knowledge bases and on the values of classification quality metrics). The following stages of research were implemented: selection of data sets from publicly available sources, formation of research knowledge bases, study of the influence of genetic algorithm parameter values on the results of knowledge base reduction, analysis of research results and development of recommendations for choosing optimal values of genetic algorithm parameters to achieve the best reduction results. Data for analysis was obtained from the public source UCI Machine Learning Repository. The selected sets are: Iris, Banknote Authentication and User Knowledge Modeling. To create research knowledge bases, the software package “Neuro-fuzzy system for generating fuzzy models for assessing the discrete state of objects” was used. Based on the analysis of the Iris data set, 3 research knowledge bases were formed, including 269, 339 and 542 fuzzy rules. For the Banknote Authentication data set, corresponding knowledge bases with 335, 643 and 1038 fuzzy rules were generated. For the User Knowledge Modeling set - with 877, 2094 and 2797 rules. For each of the knowledge bases, the values of the classification quality metrics Accuracy, Precision, Recall and F1-Score were calculated on the training and test samples. The reduction of knowledge bases was carried out in two successive stages, at each of which the corresponding genetic algorithm was used. Based on the results of the research, recommendations were developed for choosing the optimal values of the parameters of each of the genetic algorithms. The reduction of research knowledge bases based on the developed methods and algorithms made it possible to significantly reduce the number of their fuzzy rules (by an average of 85%) and at the same time slightly increase the generalizing ability of fuzzy models (by an average of 1.5%). In general, this made it possible to reduce the complexity of fuzzy models and their constituent knowledge bases, increase the accuracy of estimating the discrete state of objects and improve the interpretability of the generated solutions.

A New Feature Extraction Approach in Classification for Improving the Accuracy in Iris Recognition

Personal identity is becoming increasingly vital to meet the increasing security standards of today's business society. Iris recognition is one of the most accurate biometric technologies currently in use. Iris recognition is employed in high-security sectors due to its dependability and flawless identification rates. The steps of iris identification, comprising image preparation, extraction of features, and classifier creation, are described thoroughly in the primary portion of this research. The feature extraction stage is the most important in an iris identification system since it extracts the iris's distinctive feature. Several methods have been devised to extract the various characteristics that are unique to everyone. Modern iris identification systems frequently use Gabor filters to identify iris textural characteristics. However, in the application, it is necessary to identify the appropriate Gabor modules and to generate a pattern of iris Gabor characteristics. This research aims to provide a novel multi-channel Gabor filter and Wavelet filter for breaking down and extracting iris data from two different iris datasets. Because wavelet is the most scalable method of image processing, the research investigates using it to create a unique pattern for the iris recognition system. The MATLAB program is used to implement these ideas. CASIA and MMU are the datasets used for this purpose, and their comparative analysis is addressed in the research. To show how well the method performs, experimental results are given. We demonstrate through experiments that the suggested approach results in excellent iris identification performance.

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.