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
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
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
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
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
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
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
The integration of AI technology with IoT devices,as in the case of Artificial Intelligence of Things(AIoT),has enabled more efficient and intelligent processing and analysis of data than traditional IoT systems.However...The integration of AI technology with IoT devices,as in the case of Artificial Intelligence of Things(AIoT),has enabled more efficient and intelligent processing and analysis of data than traditional IoT systems.However,the use of biometric information by AIoT devices can pose new security risks,such as presentation attacks and privacy breaches,particularly for immutable features such as iris information,which can lead to long-term security vulnerabilities when compromised.Most existing iris recognition system security models are currently designed to address only direct presentation attack algorithms.Therefore,such models cannot address other security threats.To address these challenges,this study proposes a hybrid iris recognition system security protection model that employs presentation attack detection,flow monitoring,and black list restrictions to enhance the overall security of AIoT devices and improve the efficiency of protection.Specifically,the model aims to prevent presentation attacks andflow attacks against the iris recognition system,which may compromise the security of biometric information.The proposed method is expected to increase AIoT devices security against potential threats to sensitive information.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
Fusion of multiple instances within a modality for biometric verification performance improvement has received considerable attention. In this letter, we present an iris recognition method based on multiinstance fusio...Fusion of multiple instances within a modality for biometric verification performance improvement has received considerable attention. In this letter, we present an iris recognition method based on multiinstance fusion, which combines the left and right irises of an individual at the matching score level. When fusing, a novel fusion strategy using minimax probability machine （MPM） is applied to generate a fused score for the final decision. The experimental results on CASIA and UBIRIS databases show that the proposed method can bring obvious performance improvement compared with the single-instance method. The comparison among different fusion strategies demonstrates the superiority of the fusion strategy based on MPM.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
An efficient and robust iris location algorithm plays a very important role in a real iris recognition system. A novel and efficient iris automatic location method is presented in this study. It includes following two...An efficient and robust iris location algorithm plays a very important role in a real iris recognition system. A novel and efficient iris automatic location method is presented in this study. It includes following two steps mainly： pu- pil location and iris outer boundary location. A digital eye image was divided into many small rectangular blocks with fixed size in the pupil location, and the block with the smallest average intensity was selected as a reference area. Then image binarization was implemented taking the average intensity of the reference area as a threshold. At last the center coordinates and radius of pupil were estimated by extending the reference area to the pupil＇s boundaries in the binary iris image. In the iris outer location, two local parts of the eye image were selected and transformed into polar coordinates from Cartesian reference. In order to detect the fainter outer boundary of the iris quickly, a novel edge detector was used to locate boundaries of the two parts. The center coordinates and radius of the iris outer boundary can be estimated using the fusion of the locating results of the two local parts and the location information of the pupil. The algorithm was tested on CASIA vl.0 and MMU vl.0 digital eye image databases and experimental results show that the proposed method has satisfying performance and good robustness.展开更多
This paper deals with an optimization design method for the Gabor filters based on the analysis of an iris texture model. By means of analyzing the properties of an iris texture image, the energy distribution regulari...This paper deals with an optimization design method for the Gabor filters based on the analysis of an iris texture model. By means of analyzing the properties of an iris texture image, the energy distribution regularity of the iris texture image measured by the average power spectrum density is exploited, and the theoretical ranges of the efficient valued frequency and orientation parameters can also be deduced. The analysis shows that the energy distribution of the iris texture is generally centralized around lower frequencies in the spatial frequency domain. Accordingly, an iterative algorithm is designed to optimize the Gabor parameter field. The experimental results indicate the validity of the theory and efficiency of the algorithm.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.
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.
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.
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.
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.
基金supported by Shanghai Engineering Research Center of Cyber and Information Security Evaluation(The Third Research Institute of Ministry of Public Security)supported by the project Analysis and Research of Attack Detection Technology in IoT Smart Devices with project No.KFKT2021-009.
文摘The integration of AI technology with IoT devices,as in the case of Artificial Intelligence of Things(AIoT),has enabled more efficient and intelligent processing and analysis of data than traditional IoT systems.However,the use of biometric information by AIoT devices can pose new security risks,such as presentation attacks and privacy breaches,particularly for immutable features such as iris information,which can lead to long-term security vulnerabilities when compromised.Most existing iris recognition system security models are currently designed to address only direct presentation attack algorithms.Therefore,such models cannot address other security threats.To address these challenges,this study proposes a hybrid iris recognition system security protection model that employs presentation attack detection,flow monitoring,and black list restrictions to enhance the overall security of AIoT devices and improve the efficiency of protection.Specifically,the model aims to prevent presentation attacks andflow attacks against the iris recognition system,which may compromise the security of biometric information.The proposed method is expected to increase AIoT devices security against potential threats to sensitive information.
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supported by the PhD Programs Foundation of Ministry of Education of China (No.20050698025)the National Natural Science Foundation of China (No.60602025).
文摘Fusion of multiple instances within a modality for biometric verification performance improvement has received considerable attention. In this letter, we present an iris recognition method based on multiinstance fusion, which combines the left and right irises of an individual at the matching score level. When fusing, a novel fusion strategy using minimax probability machine （MPM） is applied to generate a fused score for the final decision. The experimental results on CASIA and UBIRIS databases show that the proposed method can bring obvious performance improvement compared with the single-instance method. The comparison among different fusion strategies demonstrates the superiority of the fusion strategy based on MPM.
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Projects 6057201 supported by the National Natural Science Foundation of ChinaLZ985-231-582627 by the 985 Special Study Project of Lanzhou University
文摘An efficient and robust iris location algorithm plays a very important role in a real iris recognition system. A novel and efficient iris automatic location method is presented in this study. It includes following two steps mainly： pu- pil location and iris outer boundary location. A digital eye image was divided into many small rectangular blocks with fixed size in the pupil location, and the block with the smallest average intensity was selected as a reference area. Then image binarization was implemented taking the average intensity of the reference area as a threshold. At last the center coordinates and radius of pupil were estimated by extending the reference area to the pupil＇s boundaries in the binary iris image. In the iris outer location, two local parts of the eye image were selected and transformed into polar coordinates from Cartesian reference. In order to detect the fainter outer boundary of the iris quickly, a novel edge detector was used to locate boundaries of the two parts. The center coordinates and radius of the iris outer boundary can be estimated using the fusion of the locating results of the two local parts and the location information of the pupil. The algorithm was tested on CASIA vl.0 and MMU vl.0 digital eye image databases and experimental results show that the proposed method has satisfying performance and good robustness.
文摘This paper deals with an optimization design method for the Gabor filters based on the analysis of an iris texture model. By means of analyzing the properties of an iris texture image, the energy distribution regularity of the iris texture image measured by the average power spectrum density is exploited, and the theoretical ranges of the efficient valued frequency and orientation parameters can also be deduced. The analysis shows that the energy distribution of the iris texture is generally centralized around lower frequencies in the spatial frequency domain. Accordingly, an iterative algorithm is designed to optimize the Gabor parameter field. The experimental results indicate the validity of the theory and efficiency of the algorithm.
