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Efficient Iris Recognition via Polar Representation and Radial Stripe Attention 认领 引用
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作者 Trong-Thua Huynh De-Thu Huynh +3 位作者 Cong-Sang Duong Hong-Son Nguyen Quoc H.Nguyen Lam-Thanh Tu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1171-1189,共19页
Deep iris recognition models are often trained on Cartesian grids,whereas iris texture follows a concentric structure with angular periodicity.This representational mismatch can weaken rotation robustness and limit pu... Deep iris recognition models are often trained on Cartesian grids,whereas iris texture follows a concentric structure with angular periodicity.This representational mismatch can weaken rotation robustness and limit pupil-to-limbus context modeling,while many pipelines still rely on accurate segmentation masks.We propose RadialFormer,an efficient mask-free iris recognition framework that performs representation learning directly in the polar domain.The pipeline first estimates pupil/iris parameters(cx,cy,rin,rout)using a percentile radial-gradient operator with anatomical constraints,and then applies a crop-based polar transform to obtain a compact 64×512unwrapped iris map.To better match polar geometry,we introduce Learnable Polar Position Encoding(LPPE)with separable radial-angular embeddings,where Fourier terms in the angular branch enforce continuity atθ=0/2π.We further propose Radial Stripe Window Attention(RSWA),which computes self-attention within full-height radial stripes and uses modular angular shifting to preserve circular consistency.Trained end-to-end with batch-hard triplet loss under P×K sampling,RadialFormer achieves 99.04%TPR@1%FPR with 0.48%EER on CASIA-V4-Lamp,and 93.63%TPR@1%FPR with 2.92%EER on CASIA-V4-Interval.Ablation and cross-dataset evaluations further validate the contributions of polar processing,LPPE,and RSWA and demonstrate robust generalization across acquisition conditions.Under the same input resolution,RadialFormer reduces computation by about 3.5x compared with a standard transformer baseline while maintaining competitive recognition accuracy. 展开更多
关键词 Iris recognition polar unwrapping vision transformer positional encoding window attention metric learning
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MobiIris: Attention-Enhanced Lightweight Iris Recognition with Knowledge Distillation and Quantization 认领 引用
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作者 Trong-Thua Huynh De-Thu Huynh +2 位作者 Du-Thang Phu Hong-Son Nguyen Quoc HNguyen 《Computers, Materials & Continua》 SCIE EI 2026年第6期623-636,共14页
This paper introduces MobiIris,a lightweight deep network for mobile iris recognition that enhances attention and specifically addresses the balance between accuracy and efficiency on devices with limited resources.Th... This paper introduces MobiIris,a lightweight deep network for mobile iris recognition that enhances attention and specifically addresses the balance between accuracy and efficiency on devices with limited resources.The proposed model is based on the large version of MobileNetV3 and adds more spatial attention blocks and an embedding-based head that was trained using margin-based triplet learning,enabling fine-grained modeling of iris textures in a compact representation.To further improve discriminability,we design a training pipeline that combines dynamic-margin triplet loss,a staged hard/semi-hard negative mining strategy,and feature-level knowledge distillation from a ResNet-50 teacher.Finally,we investigate the use of post-training float16 quantization to reduce memory footprint and latency for deployment on mobile hardware.Experiments on the challenging CASIA-IrisV4-Thousand dataset show that the full-precision MobiIris model requires only 12 MB of storage and 27 ms inference latency,while achieving an EER of 1.409%,VR@FAR=1%of 98.184%,and CMC@1 of 94.785%,closely matching a ResNet-50 baseline that is more than 7×larger and slower.Under post-training quantization,the model shrinks to 5.94 MB with 13 ms latency and maintains a competitive balance between accuracy and efficiency compared to other optimized variants.These results demonstrate that a coherent combination of lightweight architecture design,attention mechanisms,metric-learning objectives,hard negative mining,and knowledge distillation yields a practical iris recognition solution suitable for secure,real-time authentication on mobile and embedded platforms. 展开更多
关键词 Iris recognition lightweight architecture model optimization attention mechanism knowledge distillation model quantization
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The Improved Characteristics of Bionic Gabor Representations by Combining with SIFT Key-points for Iris Recognition 认领 引用 被引量：6
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作者 Yuanning Liu Fei He +4 位作者 Xiaodong Zhu Zhen Liu Ying Chen Ye Han Lijiao Yu 《Journal of Bionic Engineering》 SCIE EI CSCD 2015年第3期504-517,共14页
Gabor filters are generally regarded as the most bionic filters corresponding to the visual perception of human. Their filtered coefficients thus are widely utilized to represent the texture information of irises. How... Gabor filters are generally regarded as the most bionic filters corresponding to the visual perception of human. Their filtered coefficients thus are widely utilized to represent the texture information of irises. However, these wavelet-based iris representations are inevitably being misaligned in iris matching stage. In this paper, we try to improve the characteristics of bionic Gabor representations of each iris via combining the local Gabor features and the key-point descriptors of Scale Invariant Feature Transformation （SIFT）, which respectively simulate the process of visual object class recognition in frequency and spatial domains. A localized approach of Gabor features is used to avoid the blocking effect in the process of image division, meanwhile a SIFT key point selection strategy is provided to remove the noises and probable misaligned key points. For the combination of these iris features, we propose a support vector regression based fusion rule, which may fuse their matching scores to a scalar score to make classification decision. The experiments on three public and self-developed iris datasets validate the discriminative ability of our multiple bionic iris features, and also demonstrate that the fusion system outperforms some state-of-the-art methods. 展开更多
关键词 iris recognition bionic Gabor features scale invariant feature transformation support vector regression score level fusion
Video Sequence-Based Iris Recognition Inspired by Human Cognition Manner 认领 引用 被引量：2
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作者 Yuanning Liu Fei He +3 位作者 Xiaodong Zhu Ying Chen We Han Yanning Fu 《Journal of Bionic Engineering》 SCIE EI CSCD 2014年第3期481-489,共9页
In video sequence-based iris recognition system,the problem of making full use of relationship and correlation among frames still remains to be solved.A brand new template level multimodal fusion algorithm inspired by... In video sequence-based iris recognition system,the problem of making full use of relationship and correlation among frames still remains to be solved.A brand new template level multimodal fusion algorithm inspired by human cognition manner is proposed.In that a non-isolated geometrical manifold,named Hyper Sausage Chain due to its sausage shape,is trained using the frames from a pattern class for representing an iris class in feature space.We can classify any input iris by observing which manifold it locates in.This process is closer to the function of human being,which takes'matter cognition'instead of'matter classification'as its basic principle.The experiments on self-developed JLUBR-IRIS dataset with several video sequences per person demonstrate the effectiveness and usability of the proposed algorithm for video sequence-based iris recognition.Fur-thermore,the comparative experiments on public CASIA-I and CASIA-V4-Interval datasets show that our method can also achieve improved performance of image-based iris recognition system,provided enough samples are involved in training stage. 展开更多
关键词 iris recognition video sequences multi-modal fusion bionic recognition hyper sausage neuron
Iris recognition:a biometric method after refractive surgery 认领 引用 被引量：3
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作者 YUAN Xiao-yan ZHOU Hao SHI Peng-fei 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2007年第8期1227-1231,共5页
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
关键词 Biometric Iris recognition Refractive surgery LASIK Myopia
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An iris recognition method based on multi-orientation features and Non-symmetrical SVM 认领 引用 被引量：1
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作者 古红英 庄越挺 潘云鹤 《Journal of Zhejiang University-SCIENCE A》 CAS 2005年第5期428-432,共5页
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
关键词 Iris recognition Steerable pyramid Variation fractal dimension Non-symmetrical Support Vector Machine (NSVM)
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An Improved Daugman Method for Iris Recognition 认领 引用 被引量：1
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作者 PENG Zhiyong WANG Hongchen +1 位作者 WU Jun LI Jun 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2015年第3期229-234,共6页
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
关键词 iris recognition iris localization iris encoding
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Design of iris recognition system based on DSP and ZigBee 认领 引用
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作者 LI Jin-ming MA Lin +2 位作者 ZHANG Shao-hua CHENG Ya-li CHENG Nai-peng 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2018年第2期169-173,共5页
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
关键词 iris recognition digital signal processor(DSP) ZigBee image block
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Efficient iris recognition via ICA feature and SVM classifier 认领 引用
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作者 王勇 许录平 《Journal of Pharmaceutical Analysis》 CAS 2007年第1期29-33,共5页
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
关键词 independent component analysis support vector machine iris recognition
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Net valence analysis of iris recognition technology-based FinTech 认领 引用
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作者 Mutaz M.Al-Debei Omar Hujran Ahmad Samed Al-Adwan 《Financial Innovation》 2024年第1期2339-2385,共47页
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
关键词 Iris recognition technology(IRT) FinTech Biometric technology Net valence Perceived value Adoption intention Jordan
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Chaotic Krill Herd with Deep Transfer Learning-Based Biometric Iris Recognition System 认领 引用
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作者 Harbi Al-Mahafzah Tamer AbuKhalil Bassam A.Y.Alqaralleh 《Computers, Materials & Continua》 SCIE EI 2022年第12期5703-5715,共13页
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
关键词 Biometric verification iris recognition deep learning parameter tuning machine learning
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A Novel Approach for Iris Recognition System Using Genetic Algorithm 认领 引用
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作者 Jayesh Mohanrao Sarwade Sandip Bankar +4 位作者 Surekha Janrao Kishor Sakure Rohini Patil Shudhodhan Bokefode Nilesh Kulal 《Journal of Artificial Intelligence and Technology》 2024年第1期9-17,共9页
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
关键词 biometrics genetics algorithm(GA) iris recognition machine learning wavelet transformation
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Performance Comparison of DCT and VQ Based Techniques for Iris Recognition 认领 引用 被引量：2
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作者 H.B.Kekre Tanuja K. Sarode +3 位作者 Vinayak Ashok Bharadi Abhishek A. Agrawal Rohan J. Arora Mahesh C. Nair 《Journal of Electronic Science and Technology》 CAS 2010年第3期223-229,共7页
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
关键词 Biometrics discrete cosine transform iris recognition Kekre＇s fast codebook generation Kekre＇s proportionate error Linde Buzonad Gray vector quantization.
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Using multi-matching system based on a simplified deformable model of the human iris for iris recognition 认领 引用 被引量：1
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作者 MING Xing , XU Tao , WANG Zheng-xuan 1 2 3 1. College of Computer Science and Technology, Nanling Campus,Jilin University, 5988 Renmin Street,Changchun 130022, P. R. China 2. College of Mechanical Science and Engineering, Nanling Campus,Jilin University, 5988 Renmin Street, Changchun 130022, P. R. China 3. College of Computer Science and Technology, Qianwei Campus,Jilin University, 10 Qianwei Road, Changchun 130012, P. R. China. 《Journal of Bionic Engineering》 EI 2004年第3期183-190,共8页
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 wav... 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
关键词 iris recognition wavelet transform feature points deformable model
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A Hybrid Iris Recognition System Model Based on Presentation Attack Detection and Traffic Monitoring Module on AIoT System 认领 引用
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作者 Jishun Liu Liang Shen +2 位作者 Haixia Wu Bei Lin Zhipeng Cui 《国际计算机前沿大会会议论文集》 2024年第2期225-236,共12页
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
关键词 Presentation Attack Detection Iris Recognition AIoT System Traffic Analysis
Optimized Features Extraction of IRIS Recognition by Using MADLA to Ensure Secure Authentication 认领 引用
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作者 S. Pravinthraja K. Umamaheswari 《Circuits and Systems》 2016年第8期1927-1933,共7页
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
关键词 GLCM Deep Learning Strong Features Extraction MADMM Iris Recognition
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Iris Recognition Based on Multilevel Thresholding Technique and Modified Fuzzy c-Means Algorithm 认领 引用
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作者 Slim Ben Chaabane Rafika Harrabi +1 位作者 Anas Bushnag Hassene Seddik 《Journal on Artificial Intelligence》 2022年第4期201-214,共14页
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
关键词 Biometric authentication recognition iris recognition statistical features feature extraction fuzzy c-means TSR sensitivity classification
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Improving iris recognition performance via multi-instance fusion at the score level 认领 引用
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作者 王风华 姚向华 韩九强 《Chinese Optics Letters》 SCIE EI CAS 2008年第11期824-826,共3页
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
关键词 FRR EER Improving iris recognition performance via multi-instance fusion at the score level MPM
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Iris Feature Extraction and Recognition Based on Fractal Dimension of Grayscale Extremums 认领 引用
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作者 刘凯 周卫东 +1 位作者 王长宇 王玉 《Journal of Measurement Science and Instrumentation》 CAS 2011年第3期235-239,共5页
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
关键词 iris recognition fractal dimension normalized correlation
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Design and Implementation of Iris Pattern Recognition Using Wireless Network System 认领 引用 被引量：1
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作者 Ali Abdulhafidh Ibrahim Thura Ali Khalaf Bashar Mudhafar Ahmed 《Journal of Computer and Communications》 2016年第7期15-21,共7页
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
关键词 Iris Recognition Genetic Algorithm Principal Components Analysis Wireless Network
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