Fusion Of Iris Research Articles

Latent fingerprint and Iris fusion for enhancement of performance of human identification system

The biometric recognition system is a highly precise human identification system as per Federal Bureau of Investigation (FBI). Latent fingerprint biometrics are an efficient human identification system for criminals based on available crime evidence shreds. However, biometric trait limitations such as noise in sensed data, lack of individuality, and intra-class variation result in a low matching score, which has a negative impact on the identification as well as investigation process. The increasing demand for biometric systems for accurate identification has resulted in the development of unimodal biometric systems to multimodal biometric systems, particularly when the biometric inputs are considered as evidence of any societal crime. This paper focuses on showing the advantage of using modal based approach over the threshold based and development of a multimodal biometric framework for achieving high accuracy in human identification. The comparison of the identification rate/recognition probability of modal-based approach (87.3%) is high in compared to threshold-based approach (80%). Latent fingerprint biometric is fused with iris biometric using score level fusion (sum rule and product rule) rules. With the experimental results, it has been found that highest accuracy rate (91.15%) in proposed identification framework can be achieved while using sum rule. The proposed framework is useful in a societal crime scenario, as assumed in this paper.

Analysis of Score Level Fusion of Biometric Features

Biometric systems have gained acceptance in a variety of industries in recent years, and they continue to improve security features for access control systems. Numerous types of monotonic biometric systems have been developed. On the other hand, these systems can only provide low- to mid-level security features. As a result, combining two or more collinear biometrics are necessitated for significantly greater functionality. In this paper, a multimodal biometric technology for iris, face, and fingerprint assimilation has offered. Here, an effective matching approach based on Principal Component Analysis that employs three biometric modes to solve this challenge: iris, face, and fingerprint. The three modalities are integrated at the score level fusion, and fusion is conducted. Here, authors have proposed a combination of Iris-Fingerprint, Iris-Face, and Face-Fingerprint to develop the model. Statistical parameters like True positive (TP), True Negative, False positive, False Negative, F1 score, Accuracy are tested for different threshold values. For Iris-Fingerprint, Iris-Face, and Face-Fingerprint, our suggested technique yields accuracy of 79 percent, 85 percent, and 82 percent, respectively. Finally, a ROC curve was created using a Linear Support Vector Machine for all of the combinations, with an Area under Curve of 0.83 for the fusion of Iris and Face.

Voter Authentication System Using Feature Level Fusion of Iris, Face and Palmprint

Abstract: The authentication scheme is an important cryptographic mechanism, through which two communication parties could authenticate each other in the open network environment. To satisfy the requirement of practical applications, many authenticationschemes using passwords and smart cards have been proposed. However,passwords might be divulged or forgotten, and smart cards might be shared, lost, or stolen. In contrast, biometric methods such as face or iris scans, have no such drawbacks. Three biometric traits are collected and stored into database at the time of enrollment. The Multimodel Biometric based user authentication systems are highly secured and efficient to use and place total trust on the authentication server where biometric verification data are stored in a database. Such systems are prone to dictionary attacks initiated at the server side. In thispaper, we propose an efficient approach based on multimodal biometrics-based user authentication and key exchange system. In this system,texture properties are extracted from the palm print, face and iris. Duringlogin procedure the mutual authentication is done between the user and server and a symmetric key is generated on both sides, which could be used for further secure communication between the user and the server can also be overcome. This system can be directly applied to strengthen existing password or biometric based systems without requiring additional computation.

Ann trained and WOA optimized feature-level fusion of iris and fingerprint

“Uni Uni-modal Biometric systems has been widely implemented for maintaining security and privacy in various applications like mobile phones, banking apps, airport access control, laptop login etc. Due to Advancement in technologies, imposters designed various ways to breach the security and most of the designed biometric applications security can be compromised. The quality of input sample also play an important role to attain the best performance in terms of improved accuracy and reduced FAR & FRR. Researchers has combined the various biometrics modalities to overcome the problems of Uni-modal biometrics. In this paper, a multi biometric feature level fusion system of Iris, and Fingerprint is presented. Due to consistency feature of fingerprint and stability feature of iris modality taken into consideration for high security applications. At pre-processing level, the atmospheric light adjustment algorithm is applied to improve the quality of input samples (Iris and Fingerprint). For feature extraction, the nearest neighbour algorithm and speedup robust feature (SURF) is applied to fingerprint and Iris data respectively. Further, for selecting the best features, the extracted features are optimized by GA algorithm. To achieve an excellent recognition rate, the iris and fingerprint data is trained by ANN algorithm. The experimental results show that the proposed system exhibits the improved performance and better security. Finally, the template is secured by applying the AES algorithm and results are compared with DES, 3DES, RSA and RC4 algorithm.

Feature Level Fusion of Iris and Sclera using Entropy Based CNN Features to Improve the Performance of Biometric Authentication

Feature Level Fusion of Iris and Sclera using Entropy Based CNN Features to Improve the Performance of Biometric Authentication

Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits.

With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.

Feature Level Fusion of Iris and Voice

Feature Level Fusion of Iris and Voice

A Watermarking Based Approach for Protection of Templates in Multimodal Biometric System

Template protection is an essential requirement in biometric based authentication systems due to security and privacy issues. These issues have been arisen because of unsafe storage and misuse of templates. Techniques for template protection are developed so that templates stored in databases remain safe and inaccessible to unauthorized users. Security and privacy are the major factors to be achieved through these techniques. This paper presents a watermarking based approach to protect templates in multimodal biometric systems. Here, iris and face biometric traits have been used. This approach has used watermarking technique to embed iris image into face image to allow their safe storage in database. At the time of authentication, both iris and face patterns are retrieved from the watermarked image and matching is performed with query images. The final decision is given by matching score obtained after fusion of iris and face matching scores at match score level. MATLAB has been used to implement the proposed approach and the observation from results confirms that it does not degrade the recognition performance of existing multimodal biometric system. Rather, it provides protection to templates and facilitates to reject the compromised templates by extending the decomposition to higher level or simply by changing the embedding and extraction algorithms.

Person identification using fusion of iris and periocular deep features

A novel method for person identification based on the fusion of iris and periocular biometrics has been proposed in this paper. The challenges for image acquisition for Near-Infrared or Visual Wavelength lights under constrained and unconstrained environments have been considered here. The proposed system is divided into image preprocessing data augmentation followed by feature learning for classification components. In image preprocessing an annular iris, the portion is segmented out from an eyeball image and then transformed into a fixed-sized image region. The parameters of iris localization have been used to extract the local periocular region. Due to different imaging environments, the images suffer from various noise artifacts which create data insufficiency and complicate the recognition task. To overcome this situation, a novel method for data augmentation technique has been introduced here. For features extraction and classification tasks well-known, VGG16, ResNet50, and Inception-v3 CNN architectures have been employed. The performance due to iris and periocular are fused together to increase the performance of the recognition system. The extensive experimental results have been demonstrated in four benchmark iris databases namely: MMU1, UPOL, CASIA-Iris-distance, and UBIRIS.v2. The comparison with the state-of-the-art methods with respect to these databases shows the robustness and effectiveness of the proposed approach.

Bimodal biometric pattern recognition system based on fusion of iris and palmprint using multi-resolution approach

A biometric pattern recognition system is a pattern recognition system which uses the biological traits to recognize individuals. Single biometric systems may not possess the required characteristics like permanence, acceptability and circumvention. In general, the performance of the unimodal biometrics systems is degraded due to noisy data acquisition from sensor, intra-class and inter-class similarities. Several such restrictions are removed when multiple sources of information are used. In this paper, a bimodal biometric system designed from iris pattern and palmprint pattern is described. The feature extractor is created from the wavelet packet analysis, and classifier is created based on neural network. Using wavelet packets and gray-level spatial dependence matrix, the iris code vector is constructed. With the help of Gabor wavelets and gray-level spatial dependence matrix, the palmprint code vector is computed. The features extracted from iris pattern and palmprint pattern are fused by feature-level fusion into a multimodal pattern vector of size 1408 bits. The recognition rate achieved by the LVQ neural network is 94.50%. This system can complete recognition in 15.25 microseconds so that it can be made use for implementing real-time recognition tasks.

Robust Iris and Fingerprint Biometric Fusion in Multimodal Feature Template Matching

A fusion scheme in multimodal biometric system is proposed in the name of Multimodal Feature Template Matching (MTM) system derived from the two picture fused values of average point and weighted assessment point. The main objective of MTM algorithm is to design the average point fusion and weighted average point by fusion of Iris and fingerprint features. The MTM algorithm is assigned with avgweights based on their verification accuracy. A hybrid approach of combining biometric image level information id presented. The raw information is merged at image level. This integration addressed, provides a small template and resilience to attacks. However it does not improve the recognition performance, but the performances is also less combination to its unimodal counterpart. Confidence level integration of compatible features of two different uncorrelated biometric traits fingerprint and iris provides sustainable improvement in performance accuracy compared to other integration methods as well as best unimodal system. Fingerprint and iris integration approaches presented in this proposed are more robust and reliable.

Deep multimodal biometric recognition using contourlet derivative weighted rank fusion with human face, fingerprint and iris images

ABSTRACT The goal of multimodal biometric recognition system is to make a decision by identifying their physiological behavioural traits. Nevertheless, the decision-making process by biometric recognition system can be extremely complex due to high dimension unimodal features in temporal domain. This paper explains a deep multimodal biometric system for human recognition using three traits, face, fingerprint and iris. With the objective of reducing the feature vector dimension in the temporal domain, first pre-processing is performed using Contourlet Transform Model. Next, Local Derivative Ternary Pattern model is applied to the pre-processed features where the feature discrimination power is improved by obtaining the coefficients that has maximum variation across pre-processed multimodality features, therefore improving recognition accuracy. Weighted Rank Level Fusion is applied to the extracted multimodal features, that efficiently combine the biometric matching scores from several modalities (i.e. face, fingerprint and iris). Finally, a deep learning framework is presented for improving the recognition rate of the multimodal biometric system in temporal domain. The results of the proposed multimodal biometric recognition framework were compared with other multimodal methods. Out of these comparisons, the multimodal face, fingerprint and iris fusion offers significant improvements in the recognition rate of the suggested multimodal biometric system.

Optimum scheme selection for face–iris biometric

Designing a new dynamic and optimal scheme for face–iris fusion based on the score level, feature level and decision level fusion is considered in this study. Prior to implementing the proposed combined level fusion, several schemes are separately implemented at each level of fusion to investigate the performance improvement of each level of fusion on face and iris modalities. In fact, the optimum scheme is constructed by selecting flexible and dynamic features and scores of face and iris biometrics and then combining the advantages of different levels of fusion. Consequently, the scheme produces a set of fast and flexible features and scores for fusion. On the other hand, the idea of threshold-optimised decisions is used in this study to fuse the optimised decisions of face and iris biometrics. Experimental results on verification rates demonstrate a significant improvement of proposed combined level fusion scheme over unimodal and multimodal fusion methods.

Bin-based classifier fusion of iris and face biometrics

Accuracy and usability are the two most important issues for a multibiometric system. Most of multibiometric systems are based on matching scores or features of multiple biometric traits. However, plenty of identity information is lost in the procedure of extracting scores or features from captured multimodal biometric data, and the loss of information stops accuracy and usability of the multibiometric system from reaching a higher level. It is believed that matching scores can recover some identity information, which has not been utilized in previous fusion work. This study proposes a framework of bin-based classifier method for the fusion of multibiometrics, to deal with this problem. The proposed method embeds matching scores into a higher-dimensional space by the bin-based classifier, and rich identity information, which is hidden in matching scores, is recovered in this new space. The recovered information is sufficient to distinguish impostors from genuine users more accurately. Therefore, the multibiometric systems which are based on such rich information, are able to achieve more accurate and reliable results. The ensemble learning method is then used to select the most powerful embedding spaces. Experimental results on the CASIA-Iris-Distance demonstrate the superiority of the proposed fusion framework.

Quality-based iris segmentation-level fusion

Iris localisation and segmentation are challenging and critical tasks in iris biometric recognition. Especially in non-cooperative and less ideal environments, their impact on overall system performance has been identified as a major issue. In order to avoid a propagation of system errors along the processing chain, this paper investigates iris fusion at segmentation-level prior to feature extraction and presents a framework for this task. A novel intelligent reference method for iris segmentation-level fusion is presented, which uses a learning-based approach predicting ground truth segmentation performance from quality indicators and model-based fusion to create combined boundaries. The new technique is analysed with regard to its capability to combine segmentation results (pupillary and limbic boundaries) of multiple segmentation algorithms. Results are validated on pairwise combinations of four open source iris segmentation algorithms with regard to the public CASIA and IITD iris databases illustrating the high versatility of the proposed method.

Fusion of Iris and Fingerprint Biometric Identifier for ATM Services: An Investigative Study

Wrongdoing at ATMs has turned into an issue across the country that appearances clients, as well as bank administrators and this money related wrongdoing case rise over and again as of late. A great deal of crooks mess with the ATM terminal and take clients’ card points of interest by illicit means. When clients’ bank card is lost and the secret key is stolen, the clients’ record is helpless against assault. Conventional ATM frameworks confirm for the most part by utilizing a card (credit, charge, or brilliant) and a secret word or PIN which doubtlessly has a few imperfections. The predominant methods of client verification, which includes the utilization of either passwords, and client IDs (identifiers) or recognizable proof cards and PINs (individual distinguishing proof numbers), experience the ill effects of a few confinements. Passwords and PINs can be unlawfully obtained by direct incognito perception. At whatever point credit and ATM cards are lost or stolen, an unapproved customer can every now and again think about the right individual codes. An implanted unique mark and iris combined biometric validation plan for robotized teller machine (ATM) managing an account framework is proposed in this paper. In this plan, a multimodal biometric system is intertwined with the ATM for individual validation to ease the security level. The paper is masterminded as follows: Section 2 gives the back ground and literature survey of ATM security and the requirement for biometrics, and the related work on biometric identifiers. Section 3 depicts the materials and techniques utilized to direct the overview. Section 4 exhibits the outcomes got and the talks on the outcomes. Section 5 winds up with conclusions.

Score Level Fusion Based Bimodal Biometric Identification Using Thepade's Sorted n-ary Block Truncation Coding with Variod Proportions of Iris and Palmprint Traits

There is increasing interest in the development of reliable, rapid and non-intrusive security control systems. Multimodal biometrics such as palmprints and iris provide highly effective automatic mechanisms for use in personal identification and helps to minimize the system error rate. This paper presents a new method for extracting features in spatial domain from palmprints and iris. Thepade's Block Truncation Coding using level 2 is taken here to reduce the feature vector size. For improving accuracy in form of genuine acceptance rate(GAR)in multimodal biometric identification we take iris and palmprint together at matching score level. The test beds of 60 pairs of Iris and Palmprint samples of 10 person (6 per person of iris as well as Palmprint) are used as test bed for experimentation. In this Paper different color spaces are considered on iris images for improvement in genuine acceptance ratio (GAR). Experimental results consider different matching score proportion of Iris: Palmprint. Using propose techniques with Iris: Palmprint Score 15:1 using TSTBTC Level2 given better performance as indicated by higher GAR values than all other considers scores. RGB color spaces for multimodal fusion of Iris: Palmprint gives high GAR than all other color spaces like YCgCb, YIQ, YCbCr, YIQ, and KLUV.

Transformation based Approach of Combining Iris and Fingerprint Biometric at Confidence Level

Unimodal Biometric systems depending on information from single trait has many limitations. These are noisy input, inability to enroll into system, unacceptable error rates, spoofing and universality of traits. Multibiometric systems is likely to enhance the recognition accuracy by integration the evidence presented by multiple sources of information. In this paper a multibiometric system using transformation based fusion of two most used biometric traits, fingerprint and iris at confidence level is proposed. Features are extracted from individual biometric modalities by efficient algorithm. These features are first matched with their corresponding templates to compute the corresponding match scores. Match scores obtained from different traits are then transformed using different techniques and combined by simple sum rule to generate a fused match score. The proposed framework is evaluated using standard database. This system overcomes limitation of unimodal biometric system and gives improved performance accuracy. An equal error rate achieved by this system is 0.400. The benefit of this approach is that, it does not require any estimation as in density based approach or a large number of training score as in classifier based approach. Image or feature level fusion is expected to result in better performance, but this approach outperforms feature level as well as decision level fusion of iris and fingerprint. General Terms Recognition, Algorithms, fingerprint, iris.

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database.

Multimodal Biometric Identification System: Fusion of Iris and Fingerprint

This paper proposes a biometric person identification system based on fusion of Iris and Fingerprint images. This paper attempts to improve the performance of iris and fingerprintbased identification system by combining the individual features obtained by implementing Iris and Fingerprint identification system. There exist techniques to provide biometric identification systems based on one or other type of modalities. But usage of multiple modalities has resulted in higher accuracy and reliability as concluded by the experiments conducted.