Iris recognition technology(IRT)-based authentication is a biometric financial technology(FinTech)application used to automate user recognition and verification.In addition to being a controversial technology with var...Iris recognition technology(IRT)-based authentication is a biometric financial technology(FinTech)application used to automate user recognition and verification.In addition to being a controversial technology with various facilitators and inhibitors,the adoption of IRT-based FinTech is driven by contextual factors,such as customer perceptions,deployed biometric technology,and financial transaction settings.Due to its controversial and contextual properties,analyzing IRT-based FinTech acceptance is challenging.This study uses a net valence framework to investigate the salient positive and negative factors influencing the intention to use IRT-based FinTech in automated teller machines(ATMs)in Jordan.This study is pertinent because there is a dearth of research on IRT-based FinTech in the relevant literature;most previous research has taken purely engineering and technical approaches.Furthermore,despite considerable investments by banks and other financial institutions in this FinTech,target user adoption is minimal,and only 6% of Jordan’s ATM transactions are currently IRT-enabled.This study employs mixed methods.In the first qualitative study,17 Jordanian customers were interviewed regarding the benefits and risks of IRT-based FinTech in ATMs.Content analyses determined the most important concepts or themes.The advantages include financial security,convenience,and FinTech-enabled hygiene,whereas the concerns include performance,financial,privacy,and physical risks.The research model is constructed based on the qualitative study and theoretical underpinnings,wherein 631 Jordanian bank customers with active ATM accounts were surveyed to validate the research model.The findings indicate that IRT-based FinTech usage in ATMs is proportional to its perceived value.In descending order of effect,financial security,FinTech-enabled hygiene,and convenience benefits positively impact perceived value.Privacy,financial,and physical risks have negative impacts on perceived value,whereas performance risk has no effect.This study contributes to the relatively untapped domain of biometric technology in information systems,with important theoretical and practical implications.展开更多
In a brand new era,with chaotic scenario that exists within the world,people are undermined with diverse psychological assaults.There have been numerous sensible approaches on the way to understand and lessen those at...In a brand new era,with chaotic scenario that exists within the world,people are undermined with diverse psychological assaults.There have been numerous sensible approaches on the way to understand and lessen those attacks.Bioscrypt developments have verified to be one of the beneficial approaches for intercepting these troubles.Identifying recognition through human iris organ is said as one of the well-known biometric strategies because of its reliability and higher accurate return in comparison to different developments.Reviewing beyond literatures,terrible imaging condition,low flexibility of version,and small length iris image dataset are the constraints desiring solutions.Among these kinds of developments,the iris popularity structures are suitable gear for the human identification.Iris popularity has been an energetic studies location for the duration of previous couple of decades,due to its extensive packages in the areas,from airports to native land protection border protection.In the past,various functions and methods for iris recognition have been presented.Despite of the very fact that there are many approaches published in this field,there are still liberal amount of problems in this methodology like tedious and computational intricacy.We suggest an all-encompassing deep learning architecture for iris recognition supported by a genetic algorithm and a wavelet transformation,which may jointly learn the feature representation and perform recognition to realize high efficiency.With just a few training photos from each class,we train our model on a well-known iris recognition dataset and demonstrate improvements over prior methods.We think that this architecture can be frequently employed for various biometric recognition jobs,assisting in the development of a more scalable and precise system.The exploratory aftereffects of the proposed technique uncover that the strategy is effective inside the iris acknowledgment.展开更多
An improved Daugman iris recognition algorithm is provided in this paper, which embodies in two aspects： 1 Improvement for iris localization and 2 The improvement for both iris encoding and matching algorithms. In St...An improved Daugman iris recognition algorithm is provided in this paper, which embodies in two aspects： 1 Improvement for iris localization and 2 The improvement for both iris encoding and matching algorithms. In Step 1, the localization and shape of the pupil are roughly determined in iris image, which is used as prior knowledge to quickly locate the inner and outer boundary of iris from rough to fine scale. Eyelids, eyelashes areas and the spot in the pupil are automatically detected and removed to improve the localization accuracy. In Step 2, the possible noise from residual eyelashes is further filtered by selecting a ＂pure＂ iris area as a reference and making a validation judgment pixel-wise. Furthermore, the validation flag for each pixel is introduced into the iris encoding and matching computation, as a result, the rejection rate of iris recognition is reduced. Compared with Daugman algorithm, iris recognition test on collected human eye images shows that our proposed algorithm has an obvious improvement both on boosting the speed and reducing the rejection rate.展开更多
Iris recognition,as a biometric method,outperforms others because of its high accuracy. Iris is the visible internal organ of human,so it is stable and very difficult to be altered. But if an eye surgery must be made ...Iris recognition,as a biometric method,outperforms others because of its high accuracy. Iris is the visible internal organ of human,so it is stable and very difficult to be altered. But if an eye surgery must be made to some individuals,it may be rejected by iris recognition system as imposters after the surgery,because the iris pattern was altered or damaged somewhat during surgery and cannot match the iris template stored before the surgery. In this paper,we originally discuss whether refractive surgery for vision correction(LASIK surgery) would influence the performance of iris recognition. And experiments are designed and tested on iris images captured especially for this research from patients before and after refractive surgery. Experiments showed that refractive surgery has little influence on iris recognition.展开更多
A new method for iris recognition using a multi-matching system based on a simplified deformable model of the human iris was proposed. The method defined iris feature points and formed the feature space based on a wa...A new method for iris recognition using a multi-matching system based on a simplified deformable model of the human iris was proposed. The method defined iris feature points and formed the feature space based on a wavelet transform. In the matching stage it worked in a crude manner. Driven by a simplified deformable iris model, the crude matching was refined. By means of such multi-matching system, the task of iris recognition was accomplished. This process can preserve the elastic deformation between an input iris image and a template and improve precision for iris recognition. The experimental results indicate the va- lidity of this method.展开更多
Iris recognition enjoys universality, high degree of uniqueness and moderate user co-operation. This makes iris recognition systems unavoidable in emerging security ＆ authentication mechanisms. An iris recognition sy...Iris recognition enjoys universality, high degree of uniqueness and moderate user co-operation. This makes iris recognition systems unavoidable in emerging security ＆ authentication mechanisms. An iris recognition system based on vector quantization （VQ） techniques is proposed and its performance is compared with the discrete cosine transform （DCT）. The proposed system does not need any pre-processing and segmentation of the iris. We have tested Linde-Buzo- Gray （LBG）, Kekre＇s proportionate error （KPE） algorithm and Kekre＇s fast codebook generation （KFCG） algorithm for the clustering purpose. Proposed vector quantization based method using KFCG requires 99.99% less computations as that of full 2-dimensional DCT. Further, the KFCG method gives better performance with the accuracy of 89.10% outperforming DCT that gives accuracy around 66.10%.展开更多
A new iris feature extraction approach using both spatial and frequency domain is presented. Steerable pyramid is adopted to get the orientation information on iris images. The feature sequence is extracted on each su...A new iris feature extraction approach using both spatial and frequency domain is presented. Steerable pyramid is adopted to get the orientation information on iris images. The feature sequence is extracted on each sub-image and used to train Support Vector Machine (SVM) as iris classifiers. SVM has drawn great interest recently as one of the best classifiers in machine learning, although there is a problem in the use of traditional SVM for iris recognition. It cannot treat False Accept and False Reject differently with different security requirements. Therefore, a new kind of SVM called Non-symmetrical SVM is presented to classify the iris features. Experimental data shows that Non-symmetrical SVM can satisfy various security requirements in iris recognition applications. Feature sequence combined with spatial and frequency domain represents the variation details of the iris patterns properly. The results in this study demonstrate the potential of our new approach, and show that it performs more satis- factorily when compared to former algorithms.展开更多
The goal of this paper is to propose a fast and accurate iris pattern recognition system by using wireless network system. This paper consists of three parts: the first part includes two methods of the iris pattern re...The goal of this paper is to propose a fast and accurate iris pattern recognition system by using wireless network system. This paper consists of three parts: the first part includes two methods of the iris pattern recognition system: Libor Masek and genetic algorithms, the second part includes the compression-decompression process of iris image using Principal Component Analysis (PCA) as a data reduction method, in order to reduce image size, and the third part talks about wireless network. In this work, an iris image is transferred across wireless network which contains two independent-parallel lines connected to the central Personal Computer (PC) in order to be recognized at the end of each line, then the results of recognition are sent back to the central PC. The proposed genetic algorithm, which is used in this paper is more accurate than Masek algorithm and has low computational time and complexity, which makes this method better than Masek method in recognizing iris patterns.展开更多
To improve flexibility and reliability of iris recognition algorithm while keeping iris recognition success rate,an iris recognition approach for combining SVM with ICA feature extraction model is presented.SVM is a k...To improve flexibility and reliability of iris recognition algorithm while keeping iris recognition success rate,an iris recognition approach for combining SVM with ICA feature extraction model is presented.SVM is a kind of classifier which has demonstrated high generalization capabilities in the object recognition problem.And ICA is a feature extraction technique which can be considered a generalization of principal component analysis.In this paper,ICA is used to generate a set of subsequences of feature vectors for iris feature extraction.Then each subsequence is classified using support vector machine sequence kernels.Experiments are made on CASIA iris database,the result indicates combination of SVM and ICA can improve iris recognition flexibility and reliability while keeping recognition success rate.展开更多
Biometric verification has become essential to authenticate the individuals in public and private places.Among several biometrics,iris has peculiar features and its working mechanism is complex in nature.The recent de...Biometric verification has become essential to authenticate the individuals in public and private places.Among several biometrics,iris has peculiar features and its working mechanism is complex in nature.The recent developments in Machine Learning and Deep Learning approaches enable the development of effective iris recognition models.With this motivation,the current study introduces a novel Chaotic Krill Herd with Deep Transfer Learning Based Biometric Iris Recognition System(CKHDTL-BIRS).The presented CKHDTL-BIRS model intends to recognize and classify iris images as a part of biometric verification.To achieve this,CKHDTL-BIRS model initially performs Median Filtering(MF)-based preprocessing and segmentation for iris localization.In addition,MobileNetmodel is also utilized to generate a set of useful feature vectors.Moreover,Stacked Sparse Autoencoder(SSAE)approach is applied for classification.At last,CKH algorithm is exploited for optimization of the parameters involved in SSAE technique.The proposed CKHDTL-BIRS model was experimentally validated using benchmark dataset and the outcomes were examined under several aspects.The comparison study results established the enhanced performance of CKHDTL-BIRS technique over recent approaches.展开更多
Accuracy and fastness of iris localization are very important in automatic iris recognition. A new fast iris localization algorithm based on improved generalized symmetry transform (GST) was proposed by utilizing (iri...Accuracy and fastness of iris localization are very important in automatic iris recognition. A new fast iris localization algorithm based on improved generalized symmetry transform (GST) was proposed by utilizing (iris) symmetry. GST was improved in three aspects:1) A new distance weight function is defined. The new weight function, which is effective in iris localization, utilized the characteristic of irises that the iris is a circular object and it has one inner boundary and one outer boundary. 2) Each calculation of the symmetry measurement of a pair of symmetry points was performed by taking one point of a pair as the starting point of the transformation. This is the most important reason for fast iris localization,due to which, repetitious computation was largely excluded. 3) A new phase weight function was proposed to adjust GST to locate circle target much better because the inner part of iris is darker than the outer part. The edge map of iris image was acquired and GST was only implemented on the edge point, which decreased computation without loss of accuracy. The modification of distance weight function and phase weight function leads to the accuracy of localization, and other ideas speed up the localization. Experiments show that the average speed of new algorithm is about 7.0—8.5 times as high as traditional ones including integro-differential operator and Hough transform method.展开更多
Due to complex computation and poor real-time performance of the traditional iris recognition system,iris feature is extracted by using amplitude and phase information of the mean image blocks based on Gabor filtering...Due to complex computation and poor real-time performance of the traditional iris recognition system,iris feature is extracted by using amplitude and phase information of the mean image blocks based on Gabor filtering on image,and the k-nearest neighbor algorithm is combined to complete iris recognition function.The recognition reduces the recognition time and improves the recognition accuracy.At the same time,identification result is transmitted to the cloud server through ZigBee network to solve diffcult wiring problem.The experiment shows the system runs stably and has fast recognition speed.It has been applied to a security system.展开更多
The numerical world is under a fast development generating facilities and threats. The recommended solutions are especially the protection of information in all its states. The levels of protection show a discrepancy ...The numerical world is under a fast development generating facilities and threats. The recommended solutions are especially the protection of information in all its states. The levels of protection show a discrepancy from an application to another;governmental, commercial or even cybercriminal. The infrastructure used in modern cryptography is based on public key cryptosystem. The problem is how to make safe the private key and to memorize it without difficulties and damages. This paper introduces a biometric solution of owner signature generating an encryption of the key using the iris recognition kept in a smart card. Several precautions were taken to guarantee the safety and the availability of the use of the private key. They are two essential goals to attest: the quality of the service and the robustness of suggested safety. Being the quality of the service, the used iris recognition is based on a new emerging method founded on Flexible-ICA algorithm. This method offers a better Equal Error rate compared to other methods previously used. This quality of recognition was also reinforced by an encoding of error using a flag and finally Reed Solomon encoder. For recommended safety, a scheme based on block encryption is used. The proposed scheme is Propagating Cipher Block chaining which offers a very propagation of a high level of confusion and diffusion. Indeed, the robustness of this cryptographic process was studied by setting up strict criteria of safety.展开更多
An iris localization scheme based on edge searching strategies is presented. First, the edge detection operator Laplacian-of-Gaussian （LOG） is used to iris original image to search its inner boundary. Then, a circle...An iris localization scheme based on edge searching strategies is presented. First, the edge detection operator Laplacian-of-Gaussian （LOG） is used to iris original image to search its inner boundary. Then, a circle detection operator is introduced to locate the outer boundary and its center, which is invariant of translation, rotation and scale. Finally, the method of curve fitting is developed in localization of eyelid. The performance of the proposed method is tested with 756 iris images from 108 different classes in CASIA Iris Database and compared with the conventional Hough transform method. The experimental results show that without loss of localization accuracy, the proposed iris localization algorithm is apparendy faster than Hough transform.展开更多
This paper present a new method based on Chaos Genetic Algorithm (CGA) to localize the human iris in a given image. First, the iris image is preprocessed to estimate the range of the iris localization, and then CGA is...This paper present a new method based on Chaos Genetic Algorithm (CGA) to localize the human iris in a given image. First, the iris image is preprocessed to estimate the range of the iris localization, and then CGA is used to extract the boundary of the ~iris . Simulation results show that the proposed algorithms is efficient and robust, and can achieve sub pixel precision. Because Genetic Algorithms (GAs) can search in a large space, the algorithm does not need accurate estimation of iris center for subsequent localization, and hence can lower the requirement for original iris image processing. On this point, the present localization algirithm is superior to Daugman's algorithm.展开更多
This paper presented an individual recognition algorithm for human iris using fractal dimension of grayscale extremums for feature extraction.Firstly,iris region was localized from an eye image with modified circle de...This paper presented an individual recognition algorithm for human iris using fractal dimension of grayscale extremums for feature extraction.Firstly,iris region was localized from an eye image with modified circle detector stemmed from Daugman’s integro-differential operator.Then,segmentation was used to extract the iris and to exclude occlusion from eyelids and eyelashes.The extracted iris was normalized and mapped to polar coordinates for matching.In feature encoding,a new approach based on fractal dimension of grayscale extremums was designed to extract textural features of iris.Finally,a normalized correlation classifier was employed to determine the agreement of two iris feature templates,and the feature template was rotated left and right to avoid the interference from rotation of eyes and tilting of head.The experimental results show that fractal dimension of grayscale extremums can extract textural features from iris image effectively,and the proposed recognition algorithm is accurate and efficient.The proposed algorithm was tested on CASIA-IrisV3-Interval iris database and the performance was evaluated based on the analysis of both False Accept Rate(FAR)and False Reject Rate(FRR)curves.Experimental results show that the proposed iris recognition algorithm is effective and efficient.展开更多
Biometrics represents the technology for measuring the characteristics of the human body.Biometric authentication currently allows for secure,easy,and fast access by recognizing a person based on facial,voice,and fing...Biometrics represents the technology for measuring the characteristics of the human body.Biometric authentication currently allows for secure,easy,and fast access by recognizing a person based on facial,voice,and fingerprint traits.Iris authentication is one of the essential biometric methods for identifying a person.This authentication type has become popular in research and practical applications.Unlike the face and hands,the iris is an internal organ,protected and therefore less likely to be damaged.However,the number of helpful information collected from the iris is much greater than the other biometric human organs.This work proposes a new iris identification model based on a multilevel thresholding technique and modified Fuzzy cmeans algorithm.The multilevel thresholding technique extracts the iris from its surroundings,such as specular reflections,eyelashes,pupils,and sclera.On the other hand,the modified Fuzzy c-means is used to combine and classify the most useful statistical features to maximize the accuracy of the collected information.Therefore,having the most optimal iris recognition.The proposed model results are validated using True Success Rate(TSR)and compared to other existing models.The results show how effective the combination of the two stages of the proposed model is:the Otsu method and modified Fuzzy c-means for the 400 tested images representing 40 people.展开更多
Nowadays, Iris recognition is a method of biometric verification of the person authentication process based on the human iris unique pattern, which is applied to control system for high level security. It is a popular...Nowadays, Iris recognition is a method of biometric verification of the person authentication process based on the human iris unique pattern, which is applied to control system for high level security. It is a popular system for recognizing humans and essential to understand it. The objective of this method is to assign a unique subject for each iris image for authentication of the person and provide an effective feature representation of the iris recognition with the image analysis. This paper proposed a new optimization and recognition process of iris features selection by using proposed Modified ADMM and Deep Learning Algorithm (MADLA). For improving the performance of the security with feature extraction, the proposed algorithm is designed and used to extract the strong features identification of iris of the person with less time, better accuracy, improving performance in access control and in security level. The evaluations of iris data are demonstrated the improvement of the recognition accuracy. In this proposed methodology, the recognition of the iris features has been improved and it incorporates into the iris recognition systems.展开更多
文摘Iris recognition technology(IRT)-based authentication is a biometric financial technology(FinTech)application used to automate user recognition and verification.In addition to being a controversial technology with various facilitators and inhibitors,the adoption of IRT-based FinTech is driven by contextual factors,such as customer perceptions,deployed biometric technology,and financial transaction settings.Due to its controversial and contextual properties,analyzing IRT-based FinTech acceptance is challenging.This study uses a net valence framework to investigate the salient positive and negative factors influencing the intention to use IRT-based FinTech in automated teller machines(ATMs)in Jordan.This study is pertinent because there is a dearth of research on IRT-based FinTech in the relevant literature;most previous research has taken purely engineering and technical approaches.Furthermore,despite considerable investments by banks and other financial institutions in this FinTech,target user adoption is minimal,and only 6% of Jordan’s ATM transactions are currently IRT-enabled.This study employs mixed methods.In the first qualitative study,17 Jordanian customers were interviewed regarding the benefits and risks of IRT-based FinTech in ATMs.Content analyses determined the most important concepts or themes.The advantages include financial security,convenience,and FinTech-enabled hygiene,whereas the concerns include performance,financial,privacy,and physical risks.The research model is constructed based on the qualitative study and theoretical underpinnings,wherein 631 Jordanian bank customers with active ATM accounts were surveyed to validate the research model.The findings indicate that IRT-based FinTech usage in ATMs is proportional to its perceived value.In descending order of effect,financial security,FinTech-enabled hygiene,and convenience benefits positively impact perceived value.Privacy,financial,and physical risks have negative impacts on perceived value,whereas performance risk has no effect.This study contributes to the relatively untapped domain of biometric technology in information systems,with important theoretical and practical implications.
文摘In a brand new era,with chaotic scenario that exists within the world,people are undermined with diverse psychological assaults.There have been numerous sensible approaches on the way to understand and lessen those attacks.Bioscrypt developments have verified to be one of the beneficial approaches for intercepting these troubles.Identifying recognition through human iris organ is said as one of the well-known biometric strategies because of its reliability and higher accurate return in comparison to different developments.Reviewing beyond literatures,terrible imaging condition,low flexibility of version,and small length iris image dataset are the constraints desiring solutions.Among these kinds of developments,the iris popularity structures are suitable gear for the human identification.Iris popularity has been an energetic studies location for the duration of previous couple of decades,due to its extensive packages in the areas,from airports to native land protection border protection.In the past,various functions and methods for iris recognition have been presented.Despite of the very fact that there are many approaches published in this field,there are still liberal amount of problems in this methodology like tedious and computational intricacy.We suggest an all-encompassing deep learning architecture for iris recognition supported by a genetic algorithm and a wavelet transformation,which may jointly learn the feature representation and perform recognition to realize high efficiency.With just a few training photos from each class,we train our model on a well-known iris recognition dataset and demonstrate improvements over prior methods.We think that this architecture can be frequently employed for various biometric recognition jobs,assisting in the development of a more scalable and precise system.The exploratory aftereffects of the proposed technique uncover that the strategy is effective inside the iris acknowledgment.
基金Supported by the National Natural Science Foundation of China(61367002)the Guangxi Key Laboratory of Automatic Detecting Technology and Instruments(YQ15108)+1 种基金the Guangxi Department of Education Foundation(KY2015YB111)the Innovation Team Foundation of Guilin University of Electronic Technology,the Foundation of Guangxi Experiment Center of Information Science,the Guangxi National Natural Science Foundation(2014GXNSFAA118302)
文摘An improved Daugman iris recognition algorithm is provided in this paper, which embodies in two aspects： 1 Improvement for iris localization and 2 The improvement for both iris encoding and matching algorithms. In Step 1, the localization and shape of the pupil are roughly determined in iris image, which is used as prior knowledge to quickly locate the inner and outer boundary of iris from rough to fine scale. Eyelids, eyelashes areas and the spot in the pupil are automatically detected and removed to improve the localization accuracy. In Step 2, the possible noise from residual eyelashes is further filtered by selecting a ＂pure＂ iris area as a reference and making a validation judgment pixel-wise. Furthermore, the validation flag for each pixel is introduced into the iris encoding and matching computation, as a result, the rejection rate of iris recognition is reduced. Compared with Daugman algorithm, iris recognition test on collected human eye images shows that our proposed algorithm has an obvious improvement both on boosting the speed and reducing the rejection rate.
基金Project supported by the National Natural Science Foundation of China (No. 60427002)the National Hi-Tech Research andDevelopment Program (863) of China (No. 2006AA01Z119)
文摘Iris recognition,as a biometric method,outperforms others because of its high accuracy. Iris is the visible internal organ of human,so it is stable and very difficult to be altered. But if an eye surgery must be made to some individuals,it may be rejected by iris recognition system as imposters after the surgery,because the iris pattern was altered or damaged somewhat during surgery and cannot match the iris template stored before the surgery. In this paper,we originally discuss whether refractive surgery for vision correction(LASIK surgery) would influence the performance of iris recognition. And experiments are designed and tested on iris images captured especially for this research from patients before and after refractive surgery. Experiments showed that refractive surgery has little influence on iris recognition.
文摘A new method for iris recognition using a multi-matching system based on a simplified deformable model of the human iris was proposed. The method defined iris feature points and formed the feature space based on a wavelet transform. In the matching stage it worked in a crude manner. Driven by a simplified deformable iris model, the crude matching was refined. By means of such multi-matching system, the task of iris recognition was accomplished. This process can preserve the elastic deformation between an input iris image and a template and improve precision for iris recognition. The experimental results indicate the va- lidity of this method.
文摘Iris recognition enjoys universality, high degree of uniqueness and moderate user co-operation. This makes iris recognition systems unavoidable in emerging security ＆ authentication mechanisms. An iris recognition system based on vector quantization （VQ） techniques is proposed and its performance is compared with the discrete cosine transform （DCT）. The proposed system does not need any pre-processing and segmentation of the iris. We have tested Linde-Buzo- Gray （LBG）, Kekre＇s proportionate error （KPE） algorithm and Kekre＇s fast codebook generation （KFCG） algorithm for the clustering purpose. Proposed vector quantization based method using KFCG requires 99.99% less computations as that of full 2-dimensional DCT. Further, the KFCG method gives better performance with the accuracy of 89.10% outperforming DCT that gives accuracy around 66.10%.
基金Project supported by the National Natural Science Foundation of China (No. 60272031), Educational Department Doctor Foundation of China (No. 20010335049), and Zhejiang Provincial Natural ScienceFoundation (No. ZD0212), China
文摘A new iris feature extraction approach using both spatial and frequency domain is presented. Steerable pyramid is adopted to get the orientation information on iris images. The feature sequence is extracted on each sub-image and used to train Support Vector Machine (SVM) as iris classifiers. SVM has drawn great interest recently as one of the best classifiers in machine learning, although there is a problem in the use of traditional SVM for iris recognition. It cannot treat False Accept and False Reject differently with different security requirements. Therefore, a new kind of SVM called Non-symmetrical SVM is presented to classify the iris features. Experimental data shows that Non-symmetrical SVM can satisfy various security requirements in iris recognition applications. Feature sequence combined with spatial and frequency domain represents the variation details of the iris patterns properly. The results in this study demonstrate the potential of our new approach, and show that it performs more satis- factorily when compared to former algorithms.
文摘The goal of this paper is to propose a fast and accurate iris pattern recognition system by using wireless network system. This paper consists of three parts: the first part includes two methods of the iris pattern recognition system: Libor Masek and genetic algorithms, the second part includes the compression-decompression process of iris image using Principal Component Analysis (PCA) as a data reduction method, in order to reduce image size, and the third part talks about wireless network. In this work, an iris image is transferred across wireless network which contains two independent-parallel lines connected to the central Personal Computer (PC) in order to be recognized at the end of each line, then the results of recognition are sent back to the central PC. The proposed genetic algorithm, which is used in this paper is more accurate than Masek algorithm and has low computational time and complexity, which makes this method better than Masek method in recognizing iris patterns.
基金This work was supported by the National Natural Science Foundation of Shaanxi Province(No.2006F01)the National Natural Science Foundation of China(No.60472085).
文摘To improve flexibility and reliability of iris recognition algorithm while keeping iris recognition success rate,an iris recognition approach for combining SVM with ICA feature extraction model is presented.SVM is a kind of classifier which has demonstrated high generalization capabilities in the object recognition problem.And ICA is a feature extraction technique which can be considered a generalization of principal component analysis.In this paper,ICA is used to generate a set of subsequences of feature vectors for iris feature extraction.Then each subsequence is classified using support vector machine sequence kernels.Experiments are made on CASIA iris database,the result indicates combination of SVM and ICA can improve iris recognition flexibility and reliability while keeping recognition success rate.
文摘Biometric verification has become essential to authenticate the individuals in public and private places.Among several biometrics,iris has peculiar features and its working mechanism is complex in nature.The recent developments in Machine Learning and Deep Learning approaches enable the development of effective iris recognition models.With this motivation,the current study introduces a novel Chaotic Krill Herd with Deep Transfer Learning Based Biometric Iris Recognition System(CKHDTL-BIRS).The presented CKHDTL-BIRS model intends to recognize and classify iris images as a part of biometric verification.To achieve this,CKHDTL-BIRS model initially performs Median Filtering(MF)-based preprocessing and segmentation for iris localization.In addition,MobileNetmodel is also utilized to generate a set of useful feature vectors.Moreover,Stacked Sparse Autoencoder(SSAE)approach is applied for classification.At last,CKH algorithm is exploited for optimization of the parameters involved in SSAE technique.The proposed CKHDTL-BIRS model was experimentally validated using benchmark dataset and the outcomes were examined under several aspects.The comparison study results established the enhanced performance of CKHDTL-BIRS technique over recent approaches.
文摘Accuracy and fastness of iris localization are very important in automatic iris recognition. A new fast iris localization algorithm based on improved generalized symmetry transform (GST) was proposed by utilizing (iris) symmetry. GST was improved in three aspects:1) A new distance weight function is defined. The new weight function, which is effective in iris localization, utilized the characteristic of irises that the iris is a circular object and it has one inner boundary and one outer boundary. 2) Each calculation of the symmetry measurement of a pair of symmetry points was performed by taking one point of a pair as the starting point of the transformation. This is the most important reason for fast iris localization,due to which, repetitious computation was largely excluded. 3) A new phase weight function was proposed to adjust GST to locate circle target much better because the inner part of iris is darker than the outer part. The edge map of iris image was acquired and GST was only implemented on the edge point, which decreased computation without loss of accuracy. The modification of distance weight function and phase weight function leads to the accuracy of localization, and other ideas speed up the localization. Experiments show that the average speed of new algorithm is about 7.0—8.5 times as high as traditional ones including integro-differential operator and Hough transform method.
文摘Due to complex computation and poor real-time performance of the traditional iris recognition system,iris feature is extracted by using amplitude and phase information of the mean image blocks based on Gabor filtering on image,and the k-nearest neighbor algorithm is combined to complete iris recognition function.The recognition reduces the recognition time and improves the recognition accuracy.At the same time,identification result is transmitted to the cloud server through ZigBee network to solve diffcult wiring problem.The experiment shows the system runs stably and has fast recognition speed.It has been applied to a security system.
文摘The numerical world is under a fast development generating facilities and threats. The recommended solutions are especially the protection of information in all its states. The levels of protection show a discrepancy from an application to another;governmental, commercial or even cybercriminal. The infrastructure used in modern cryptography is based on public key cryptosystem. The problem is how to make safe the private key and to memorize it without difficulties and damages. This paper introduces a biometric solution of owner signature generating an encryption of the key using the iris recognition kept in a smart card. Several precautions were taken to guarantee the safety and the availability of the use of the private key. They are two essential goals to attest: the quality of the service and the robustness of suggested safety. Being the quality of the service, the used iris recognition is based on a new emerging method founded on Flexible-ICA algorithm. This method offers a better Equal Error rate compared to other methods previously used. This quality of recognition was also reinforced by an encoding of error using a flag and finally Reed Solomon encoder. For recommended safety, a scheme based on block encryption is used. The proposed scheme is Propagating Cipher Block chaining which offers a very propagation of a high level of confusion and diffusion. Indeed, the robustness of this cryptographic process was studied by setting up strict criteria of safety.
文摘An iris localization scheme based on edge searching strategies is presented. First, the edge detection operator Laplacian-of-Gaussian （LOG） is used to iris original image to search its inner boundary. Then, a circle detection operator is introduced to locate the outer boundary and its center, which is invariant of translation, rotation and scale. Finally, the method of curve fitting is developed in localization of eyelid. The performance of the proposed method is tested with 756 iris images from 108 different classes in CASIA Iris Database and compared with the conventional Hough transform method. The experimental results show that without loss of localization accuracy, the proposed iris localization algorithm is apparendy faster than Hough transform.
文摘This paper present a new method based on Chaos Genetic Algorithm (CGA) to localize the human iris in a given image. First, the iris image is preprocessed to estimate the range of the iris localization, and then CGA is used to extract the boundary of the ~iris . Simulation results show that the proposed algorithms is efficient and robust, and can achieve sub pixel precision. Because Genetic Algorithms (GAs) can search in a large space, the algorithm does not need accurate estimation of iris center for subsequent localization, and hence can lower the requirement for original iris image processing. On this point, the present localization algirithm is superior to Daugman's algorithm.
基金supported by the Independent Innovation Foundation of Shandong University(No.2009JC004)the Program of Development of Science and Technology of Shandong(No.2010GSF10243)
文摘This paper presented an individual recognition algorithm for human iris using fractal dimension of grayscale extremums for feature extraction.Firstly,iris region was localized from an eye image with modified circle detector stemmed from Daugman’s integro-differential operator.Then,segmentation was used to extract the iris and to exclude occlusion from eyelids and eyelashes.The extracted iris was normalized and mapped to polar coordinates for matching.In feature encoding,a new approach based on fractal dimension of grayscale extremums was designed to extract textural features of iris.Finally,a normalized correlation classifier was employed to determine the agreement of two iris feature templates,and the feature template was rotated left and right to avoid the interference from rotation of eyes and tilting of head.The experimental results show that fractal dimension of grayscale extremums can extract textural features from iris image effectively,and the proposed recognition algorithm is accurate and efficient.The proposed algorithm was tested on CASIA-IrisV3-Interval iris database and the performance was evaluated based on the analysis of both False Accept Rate(FAR)and False Reject Rate(FRR)curves.Experimental results show that the proposed iris recognition algorithm is effective and efficient.
基金This research is supported by the faculty of computers and information Technology and the Industrial Innovation and Robotics Center,University of Tabuk.
文摘Biometrics represents the technology for measuring the characteristics of the human body.Biometric authentication currently allows for secure,easy,and fast access by recognizing a person based on facial,voice,and fingerprint traits.Iris authentication is one of the essential biometric methods for identifying a person.This authentication type has become popular in research and practical applications.Unlike the face and hands,the iris is an internal organ,protected and therefore less likely to be damaged.However,the number of helpful information collected from the iris is much greater than the other biometric human organs.This work proposes a new iris identification model based on a multilevel thresholding technique and modified Fuzzy cmeans algorithm.The multilevel thresholding technique extracts the iris from its surroundings,such as specular reflections,eyelashes,pupils,and sclera.On the other hand,the modified Fuzzy c-means is used to combine and classify the most useful statistical features to maximize the accuracy of the collected information.Therefore,having the most optimal iris recognition.The proposed model results are validated using True Success Rate(TSR)and compared to other existing models.The results show how effective the combination of the two stages of the proposed model is:the Otsu method and modified Fuzzy c-means for the 400 tested images representing 40 people.
文摘Nowadays, Iris recognition is a method of biometric verification of the person authentication process based on the human iris unique pattern, which is applied to control system for high level security. It is a popular system for recognizing humans and essential to understand it. The objective of this method is to assign a unique subject for each iris image for authentication of the person and provide an effective feature representation of the iris recognition with the image analysis. This paper proposed a new optimization and recognition process of iris features selection by using proposed Modified ADMM and Deep Learning Algorithm (MADLA). For improving the performance of the security with feature extraction, the proposed algorithm is designed and used to extract the strong features identification of iris of the person with less time, better accuracy, improving performance in access control and in security level. The evaluations of iris data are demonstrated the improvement of the recognition accuracy. In this proposed methodology, the recognition of the iris features has been improved and it incorporates into the iris recognition systems.
