Iris Template Research Articles

A cancellable iris template protection scheme based on inverse merger and Bloom filter

Iris recognition has found extensive applications in real-world situations and financial contexts. However, Iris template protection schemes are highly vulnerable to well-planned attacks that can lead to the leakage of personal information. Once biological information is compromised, this loss is irreversible for the individual. Cancelable protection schemes for iris templates based on the Bloom filter have substantial attention in the field of iris biometrics. Nevertheless, Bloom filter-based template protection schemes face specific security challenges. Therefore, it is crucial to propose a method to protect iris templates that is both secure and efficient. To address irreversible limitations in security analysis, we propose a template protection scheme, a cancelable iris biometric protection scheme based on inverse merger and Bloom filter. The primary idea of the proposed scheme is to perform an inverse merger operation on the acquired codewords before mapping the iris templates to the Bloom filter specifically. Through a comparison of the sizes between the original templates and their inverted counterparts, the template with the smaller size is chosen as the definitive result, subsequently being mapped into the Bloom filter. Our proposed scheme exhibits significant advancements in accuracy across multiple datasets, as evidenced by empirical validations. In the optimal case, our model achieves an excellent performance of 98.04% in terms of GAR, while achieving a significant reduction of 0.51% in terms of EER. Furthermore, a comparative analysis with existing iris template protection methods is performed to evaluate its relative effectiveness in resisting the attack of averaging the columns of a block. The results demonstrate that the scheme exhibits robust resistance to such attacks. The experimental analysis demonstrated that the scheme provided a good balance between accuracy and safety.

Effect of Particle Swarm Optimization Convolutional Neural Network in An Iris Recognition System

An iris recognition system based on Convolutional Neural Network with Particle Swarm Optimization (CNN-PSO) was developed to improve the identified hitches in the existing systems. Iris images of 150 and 108 persons were acquired from LAUIRIS (Nigeria) and CASIA (China) respectively. The images were resized and cropped after which Hough transform was used for effective localization of the iris region and normalised using Daugman’s rubber sheet model, while an efficient Cumulative Sum-based analysis method was used to extract discriminative features from the normalised iris images after which the iris code was generated. The iris code generated in a vector form was optimised with PSO after which they are fed into Convolutional neural network; the same procedure was engaged during enrolment and authentication to generate the iris template. Euclidean distance was used for decision making on test sample template and stored template. The system was implemented with MATLAB R2013a. The performance of the developed system was evaluated on LAURIS and CASIA, and compared with the existing systems (CNN, BPNN-PSO and BPNN) using False Acceptance Rate (FAR), False Rejection Rate (FRR) and Recognition Rate (RR). CNN-PSO has the highest recognition rate of 98.67% and 97.22% for LAUIRIS and CASIA respectively among the systems which showed an improvement over other three recognition technique. The developed CNN-PSO has not only produced an improved Iris recognition system over the others, with the highest recognition rate for both datasets but it also provides a significant recognition rate of black Iris images despite the limitations identified with black Iris images in separating Iris image from other part of the eyes. The developed

Effect of Particle Swarm Optimization Convolutional Neural Network in An Iris Recognition System

An iris recognition system based on Convolutional Neural Network with Particle Swarm Optimization (CNN-PSO) was developed to improve the identified hitches in the existing systems. Iris images of 150 and 108 persons were acquired from LAUIRIS (Nigeria) and CASIA (China) respectively. The images were resized and cropped after which Hough transform was used for effective localization of the iris region and normalised using Daugman’s rubber sheet model, while an efficient Cumulative Sum-based analysis method was used to extract discriminative features from the normalised iris images after which the iris code was generated. The iris code generated in a vector form was optimised with PSO after which they are fed into Convolutional neural network; the same procedure was engaged during enrolment and authentication to generate the iris template. Euclidean distance was used for decision making on test sample template and stored template. The system was implemented with MATLAB R2013a. The performance of the developed system was evaluated on LAURIS and CASIA, and compared with the existing systems (CNN, BPNN-PSO and BPNN) using False Acceptance Rate (FAR), False Rejection Rate (FRR) and Recognition Rate (RR). CNN-PSO has the highest recognition rate of 98.67% and 97.22% for LAUIRIS and CASIA respectively among the systems which showed an improvement over other three recognition technique. The developed CNN-PSO has not only produced an improved Iris recognition system over the others, with the highest recognition rate for both datasets but it also provides a significant recognition rate of black Iris images despite the limitations identified with black Iris images in separating Iris image from other part of the eyes. The developed technique can be applied to various field of life like security, surveillance systems.

Enhancing Iris Authentication for Managing Multiple Cancellations: Leveraging Quotient Filters

Biometric authentication methods have become increasingly popular for their ability to offer secure and convenient access control. However, concerns about the privacy and security of biometric data have arisen. In this study, we present a novel approach to address these concerns by proposing a cancellable biometric encryption technique for secure and format-preserving iris authentication. Our method leverages the Quotient Filter data structure to transform encrypted iris templates into cancellable templates while preserving their original format. We carefully select an appropriate format-preserving encryption algorithm for iris templates and design a mapping scheme to achieve cancellability. To assess the effectiveness and performance of our approach, extensive experiments are conducted. The quantitative results indicate the efficiency and efficacy of our cancellable biometric encryption technique using the Quotient Filter. Our innovation, the Iris Authentication for Multiple Cancelled Instances Using a Quotient Filter (IAMCIQF), demonstrates competitive performance across several key metrics. IAMCIQF achieves a high level of security strength and strikes a balance between security and efficiency in terms of key size, encryption and decryption speeds and storage efficiency when compared to other existing techniques. The quantitative outcomes underscore IAMCIQF’s potential as a promising solution for attaining secure and format-preserving iris authentication, addressing critical concerns about biometric data security.

A standard ranking algorithm for robust iris template protection

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

An effective iris biometric privacy protection scheme with renewability

Iris is considered a unique, robust biometric that can be used as an authentication factor. Privacy protection of individuals’ iris templates is an essential requirement. The essential attributes needed to ensure privacy of biometrics are irreversibility, renewability, and unlinkability. Information distortion is an effective way to achieve the privacy. However, this may lead to some degradation of recognition performance. A trade-off between recognition performance and the privacy level provided by the authentication system arises when it tries to provide both simultaneously. This paper proposes a novel approach that ensures the privacy of each individual's iris by distorting iris feature information. At the meantime, this approach balances the trade-off mentioned above up to a tolerable level. The binary feature templates of iris biometrics are transformed into another irreversible binary representation by manipulating them block-wise. The results obtained against two datasets using proposed approach illustrate excessive Genuine Acceptance Rates (GAR) when the approach accepts imposters at a rate which is reduced to 0.01 %. The proposed approach satisfies the three attributes that a biometric system should fulfil, namely irreversibility, renewability, and unlinkability. The renewability attribute induces the cancellability of iris biometric template. Compared to the existing works, the proposed approach maintains Equal Error Rate (EER) at 0.18 %.

Iris Recognition System Based on Zak-Gabor Wavelet Packets

The paper proposes a new iris coding method based on Zak-Gabor wavelet packet transform. The essential component of the iris recognition methodology design is an effective adaptation of the transformation parameters that makes the coding sensitive to the frequencies characterizing ones eye. We thus propose to calculate the between-to-within class ratio of weakly correlated Zak-Gabor transformation coefficients allowing for selection the frequencies the most suitable for iris recognition. The Zak-Gabor-based coding is non-reversible, i.e., it is impossible to reconstruct the original iris image given the iris template. Additionally, the inference about the iris image properties from the Zak-Gabor-based code is limited, providing a possibility to embed the biometric replay attack prevention methodology into the coding. We present the final prototype system design, including the hardware, and evaluate its performance using the database of 720 iris images.

Effect of Hadamard multiplication on bloom filter and double bloom filter transformations

AbstractIris biometric is the most common and widely accepted biometric authentication method due to its high accuracy. The iris biometric template should be protected to overcome security and privacy attacks. The two standard iris biometric template protection methods are bloom filter and double bloom filter‐based feature transformations. In this article, we first permuted and then applied the p‐order Hadamard product on the iris template before the bloom filter and double bloom filter‐based transformation to enhance the security. The performance and security are measured using the CASIAv1 iris benchmark database to study the effect of Hadamard multiplication on bloom and double bloom filter‐based transformation. The Hadamard multiplication on the iris template increases the security of the transformed iris template and slightly degrades the matching performance.

Iris Biometric Watermarking for Authentication Using Multiband Discrete Wavelet Transform and Singular-Value Decomposition

The most advanced technology, watermarking enables intruders to access the database. Various techniques have been developed for information security. Watermarks and histories are linked to many biometric techniques such as fingerprints, palm positions, gait, iris and speech are recommended. Digital watermarking is the utmost successful approaches among the methods available. In this paper the multiband wavelet transforms and singular value decomposition are discussed to establish a watermarking strategy rather than biometric information. The use of biometrics instead of conservative watermarks can enhance information protection. The biometric technology being used is iris. The iris template can be viewed as a watermark, while an iris mode of communication may be used to help information security with the addition of a watermark to the image of the iris. The research involves verifying authentication against different attacks such as no attacks, Jpeg Compression, Gaussian, Median Filtering and Blurring. The Algorithm increases durability and resilience when exposed to geometric and frequency attacks. Finally, the proposed framework can be applied not only to the assessment of iris biometrics, but also to other areas where privacy is critical.

Biometric iris templates security based on secret image sharing and chaotic maps

Biometric technique includes of uniquely identifying person based on their physical or behavioural characteristics. It is mainly used for authentication. Storing the template in the database is not a safe approach, because it can be stolen or be tampered with. Due to its importance the template needs to be protected. To treat this safety issue, the suggested system employed a method for securely storing the iris template in the database which is a merging approach for secret image sharing and hiding to enhance security and protect the privacy by decomposing the template into two independent host (public) iris images. The original template can be reconstructed only when both host images are available. Either host image does not expose the identity of the original biometric image. The security and privacy in biometrics-based authentication system is augmented by storing the data in the form of shadows at separated places instead of whole data at one. The proposed biometric recognition system includes iris segmentation algorithms, feature extraction algorithms, a (2, 2) secret sharing and hiding. The experimental results are conducted on standard colour UBIRIS v1 data set. The results indicate that the biometric template protection methods are capable of offering a solution for vulnerability that threatens the biometric template.

Alignment-Robust Cancelable Biometric Scheme for Iris Verification

In this paper, we propose a histogram of oriented gradient inspired cancelable biometrics - Random Augmented Histogram of Gradients (R∙HoG) for iris template protection. The proposed R∙HoG is built upon on two main components: 1) column vector random augmentation and 2) gradient orientation grouping mechanisms to transform the unaligned irisCode feature into the alignment-robust cancelable template. The alignment-robust property of the proposed R∙HoG enables the fast template comparison which is crucial for an efficient authentication process. Experiments were performed on CASIA-IrisV3-Internal and CASIA-IrisV4-Thousand datasets. The results demonstrate the proposed R∙HoG could achieve acceptable verification performance in both datasets. Other than that, the irreversibility and security properties are studied based on major security and privacy attacks in biometric system. Lastly, results from the benchmarking evaluation framework show the proposed method is satisfying the unlinkability property.

Iris Image Preprocessing and Recognition System

Introduction: Iris recognition framework has gotten vital, particularly in the field of safety, since it gives high reliability. Iris surface is a natural secret phrase that enjoys incredible benefits like inconstancy, soundness, unique highlights for every individual, and its significance in the security field. This makes an iris acknowledgment framework upper of other biometric strategies utilized for human identification. Iris is a hued muscle present inside the eye which helps in controlling the measure of light entering the eye. It has a few extraordinary textural data, which doesn’t get modified or altered effectively, making it a most appropriate quality for biometric frameworks. Because of its uniqueness, all-inclusiveness, unwavering quality, and strength, Iris designs serve a significant job in potential acknowledgment or verification applications. Numerous analysts have proposed new techniques to the iris acknowledgment framework to expand the productivity of the framework. Methods: A total of 460 images were taken into account. First and foremost picture pre-processing is done on the information picture to eliminate undesirable clamor from it and then different segmentation strategies, for example, edge recognition, Camus and Wildes, and so forth are applied for the proficient identification of the inner and outer boundary of the iris. Results: Iris Segmentation was done for MMU Database which contains 460 iris images. Results are recorded. Results from these indicate that it has over Seventy six percentage. Furthermore, same Wildes methods can be done with different databases like CASIA, IITD, UPOL. Results may vary from MMU database. Conclusion: Our study showed more than seventy six percentage of success rate for this particular database. Live iris templates also can be obtained and segmentation techniques can be used and find the accuracy.

DenseSENet: more accurate and robust cross-domain iris recognition

With the broad application of iris recognition systems, iris images acquired under cross-sensor and cross-spectrum conditions are increasingly common. Differences in the information stored in these images place high requirements for accuracy and robustness on cross-domain iris recognition algorithms, and current algorithms have not performed well in cross-domain iris recognition. Hence, we propose a dense squeeze and excitation network (DenseSENet) to extract common features in cross-domain iris images and use real-valued features for cross-domain matching. DenseSENet is a deep convolutional neural network based on an attention mechanism. It uses the batch normalization-rectified linear unit-Conv1 × 1 structure to increase the nonlinearity of the network and repeatedly uses features through dense connections to effectively prevent overfitting. The proposed dense squeeze and excitation structure extracts more accurate and stable features by learning the relations between channels and assigning corresponding attention values. The structure of global average pooling-fully connected-softmax is utilized to obtain compact and normalized real-valued feature templates to effectively reduce the size of the iris template. The proposed cross-domain iris recognition framework based on DenseSENet is end-to-end, which transforms the cross-domain matching problem to a many-to-one classification problem. Experiments are conducted on publicly available cross-sensor and cross-spectral iris databases. The results show that the proposed real-valued feature matching based on DenseSENet has better matching accuracy and robustness than the state-of-art cross-domain matching algorithm on two publicly available cross-sensor and cross-spectral iris databases.

A fuzzy vault development based on iris images

Biometric systems gather information from the person's biometric attributes, used extensively to authorize the individuals. Due to the obvious convenience of using specific individual traits such as face, fingerprints, palm veins, and irises, biometric authentication is becoming more common. In particular, Iris systems are in high demand for high-assurance applications, because they contain a broad feature set and remain stable. Authentication methods based on iris biometrics are now commonly used in a variety of fields. This is due to the fact that iris biometric authentication is both safer and more comfortable than conventional passwords. Template Security is a major concern in biometric systems. The template security mechanism ensures reusable, permanent, and un-linkable models. The Fuzzy Vault strategy is one of the most popular security schemes for Template protection.
 Fuzzy vault has demonstrated to be an effective protection method but lacks revocability and security attacks. This article introduced an improved fuzzy vault system. The improved fuzzy vault system was introduced, which uses more than one key to protect biometric data. Different keys make the search space more detailed. The additional key was used to encrypt vault data, which stopped the intruder from accessing the information on the person's biometry. The system was tested using CASIA.v1 and IITD.v1 datasets, and findings showed that the system ensures the protection and authentication of the iris templates without compromising performance. The proposed modification gave a 0.0 % False Accepted Rate (FAR) for both the dataset and False Rejected Rate (FRR), 0.14 % for CASIA v1 and 0.12 % for ITTD v1 False Rejected rate

SviaB: Secure and verifiable multi‐instance iris remote authentication using blockchain

Homomorphic encryption (HE) is the most widely explored research area in the construction of privacy-preserving biometric authentication systems because of its advantages over cancellable biometrics and biometric cryptosystems. However, most of the existing privacy-preserving biometric authentication systems using HE assume that the server performs computations honestly. In a malicious server setting, the server may return an arbitrary result to save computational resources, resulting in a false accept/reject. To address this, secure and verifiable multi-instance iris authentication using blockchain (SviaB) is proposed. Paillier HE provides confidentiality for the iris templates in SviaB. The blockchain offers the integrity of the encrypted reference iris templates as well as the trust of the comparator result. The challenges of using blockchain in biometrics are also addressed in SviaB. Extensive experimental results on benchmark iris databases demonstrate that SviaB provides privacy to the iris templates with no loss of accuracy and trust in the comparator result.

An efficient CNN based encrypted Iris recognition approach in cognitive-IoT system

Recently, biometric-based security plays a vital role in the success of the Cognitive Internet of Things (C-IoT) based security framework. The iris trait solves a lot of security issues, especially in smart IoT-based applications. It increases the resistance of these systems against severe authentication attacks. In this paper, an efficient iris recognition model based on chaotic encryption and deep Convolutional Neural Networks (CNNs) is proposed for C-IoT applications. CNN is used to extract the deep iris features from the left and right eyes, which will be used as input features to a fully connected neural network with a Softmax classifier. CASIA V4 Interval dataset and Phoenix dataset are used to train the CNN model; to get the best tuning of network parameters. In this paper, the effect of adding different kinds of noise to iris images, due to noise interference related to sensing IoT devices, bad acquisition of iris images by system users, or other system assaults, is discussed. This strategy of noisy encrypted iris images is evaluated over the internet environment. Chaotic encryption is utilized to secure the transmission of iris templates in the proposed model. The results showed that the proposed approach attains supreme accuracy compared to the existing approaches, it is obtained up to 99.24% and 100% with CASIA V4 and Phoenix datasets, respectively. The proposed model achieves satisfied and competitive results regard accuracy, and robustness among existing methods. Regards to recognition accuracy rate, this methodology shows low degradation of recognition accuracy rates in the case of using noised iris images. Likewise, the proposed method has a relatively low training time, which is a useful parameter in critical IoT based uses such as Tele-Medicine application.

An efficient multi-factor authentication scheme based CNNs for securing ATMs over cognitive-IoT.

Nowadays, the identity verification of banks’ clients at Automatic Teller Machines (ATMs) is a very critical task. Clients’ money, data, and crucial information need to be highly protected. The classical ATM verification method using a combination of credit card and password has a lot of drawbacks like Burglary, robbery, expiration, and even sudden loss. Recently, iris-based security plays a vital role in the success of the Cognitive Internet of Things (C-IoT)-based security framework. The iris biometric eliminates many security issues, especially in smart IoT-based applications, principally ATMs. However, integrating an efficient iris recognition system in critical IoT environments like ATMs may involve many complex scenarios. To address these issues, this article proposes a novel efficient full authentication system for ATMs based on a bank’s mobile application and a visible light environments-based iris recognition. It uses the deep Convolutional Neural Network (CNN) as a feature extractor, and a fully connected neural network (FCNN)—with Softmax layer—as a classifier. Chaotic encryption is also used to increase the security of iris template transmission over the internet. The study and evaluation of the effects of several kinds of noisy iris images, due to noise interference related to sensing IoT devices, bad acquisition of iris images by ATMs, and any other system attacks. Experimental results show highly competitive and satisfying results regards to accuracy of recognition rate and training time. The model has a low degradation of recognition accuracy rates in the case of using noisy iris images. Moreover, the proposed methodology has a relatively low training time, which is a useful parameter in a lot of critical IoT based applications, especially ATMs in banking systems.

Iris based cancelable biometric cryptosystem for secure healthcare smart card

Health related information of an individual is very sensitive and demands a high level of security and privacy. Healthcare providers have the responsibility to ensure that patient information is secure and accessible only to authorized users. Healthcare systems are using biometrics since long for authentication and/or access control purposes. Biometrics can also be used for healthcare data security and privacy. This paper proposes an iris based cancelable biometric cryptosystem to securely store the healthcare data of patients on the smart card. It employs symmetric key cryptography to encrypt the healthcare data and store it on the smart card in encrypted form. We use the fuzzy commitment scheme to bind the secret encryption key with the cancelable iris template of the patient. Our proposed scheme provides user authentication as well as the decryption of healthcare data when needed by using the iris template of the owner of the healthcare smart card. The implementation results show that our proposed scheme provides better performance as compared to other schemes. It can generate an encryption key of a maximum of 252 bits from the input iris template with a false acceptance rate (FAR) of 0 and a false rejection rate (FRR) of 0.07. The generated key can be used for encrypting the health care data of patients using a symmetric encryption algorithm, e.g. Advance Encryption Standard (AES), International Data Encryption Algorithm (IDEA), Blowfish, etc. As compared to a conventional encryption system where the security of the system depends on keeping the key secret, our proposed scheme binds the encryption key with the iris - template of the patient impeccably without the need to store it securely. The security analysis demonstrates that it is not possible for an attacker to retrieve the secret key or healthcare data of the patient from the stolen healthcare card.

Privacy-preserving and verifiable multi-instance iris remote authentication using public auditor

Homomorphic Encryption (HE) is the most widely explored research area to construct privacy-preserving biometric authentication systems due to its advantages over cancelable biometrics and biometric cryptosystem. However, most of the existing privacy-preserving biometric authentication systems using HE assume that the server performs computations honestly. In a malicious server setting, the server may return an arbitrary result to save the computational resources results in false accept/reject. To address this, we propose a privacy-preserving and verifiable multi-instance iris authentication using public auditor (PviaPA). Paillier HE provides confidentiality for the iris templates in PviaPA. A public auditor ensures the correctness of comparator result in PviaPA. Extensive experimental results on benchmark iris databases demonstrate that PviaPA provides privacy to the iris templates with no loss in the accuracy as well as trust on the comparator result.

Cancelable Iris template for secure authentication based on random projection and double random phase encoding

Biometric recognition approaches have overwhelmed major issues in traditional authentication systems. Privacy invasion and security are the needed features in the effective development of biometric recognition systems. Recently some biometric techniques have been suffered to hacking trails also. Hence there is a despairing need to propose a novel cancelable iris recognition scheme. The prime objective of this research is to create a transformed cancelable biometric template through an encryption function and a one-way transformation function. The random projection matrix is employed here to generate the first feature vector. Then an encrypted cancelable iris code is generated using Double Random Phase Encryption (DRPE) in the Fractional Fourier Transform (FFT) domain. The proposed framework utilizes both the left and right iris image of a person and generates a single cancelable iris template. This feature guarantees the privacy-preserving cancelable iris code generation for secure authentication. The experiment is carried on two state-of-art datasets CASIA Iris V4 and IITD iris to affirm the efficacy of the proposed methodology. The result analysis witnessed a promising accuracy of 99.59%, a recognition rate of 99.88% with a lower Equal Error Rate (ERR) of 0.46%. It has also been proved that the proposed approach is computationally efficient as it recognizes the iris code with less recognition time of 7 ms with a maximum true positive and true negative rate of 100%.