Iris Information Research Articles

Generative network copyright protection method based on iris information watermarking

Generative network copyright protection method based on iris information watermarking

Adversarial attack vulnerability for multi‐biometric authentication system

AbstractResearch on multi‐biometric authentication systems using multiple biometric modalities to defend against adversarial attacks is actively being pursued. These systems authenticate users by combining two or more biometric modalities using score or feature‐level fusion. However, research on adversarial attacks and defences against each biometric modality within these authentication systems has not been actively conducted. In this study, we constructed a multi‐biometric authentication system using fingerprint, palmprint, and iris information from CASIA‐BIT by employing score and feature‐level fusion. We verified the system's vulnerability by deploying adversarial attacks on single and multiple biometric modalities based on the FGSM, with epsilon values ranging from 0 to 0.5. The experimental results show that when the epsilon value is 0.5, the accuracy of the multi‐biometric authentication system against adversarial attacks on the palmprint and iris information decreases from 0.995 to 0.018 and 0.003, respectively, and the f1‐score decreases from 0.995 to 0.007 and 0.000, respectively, demonstrating susceptibility to adversarial attacks. In the case of fingerprint data, however, the accuracy and f1‐score decreased from 0.995 to 0.731 and from 0.995 to 0.741, respectively, indicating resilience against adversarial attacks.

An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network

The process of iris recognition can result in a decline in recognition performance when the resolution of the iris images is insufficient. In this study, a super-resolution model for iris images, namely SwinGIris, which combines the Swin Transformer and the Generative Adversarial Network (GAN), is introduced. SwinGIris performs quadruple super-resolution reconstruction for low-resolution iris images, aiming to improve the resolution of iris images and thereby improving the recognition accuracy of iris recognition systems. The model utilizes residual Swin Transformer blocks to extract depth global features, and the progressive upsampling method along with sub-pixel convolution is conducive to focusing on the high-frequency iris information in the presence of more non-iris information. In order to preserve high-frequency details, the discriminator employs a VGG-style relative classifier to guide the generator in generating super-resolution images. In experimental section, we enhance low-resolution (56 × 56) iris images to high-resolution (224 × 224) iris images. Experimental results indicate that the SwinGIris model achieves satisfactory outcomes in restoring low-resolution iris image textures while preserving identity information.

Impact of Occlusion Masks on Gender Classification from Iris Texture

Gender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non-iris elements. When, the goal is to classify the gender using exclusively the iris texture, the presence of gender information in the masks may result in apparently higher accuracy, thereby not reflecting the actual gender information present in the iris. However, no measures have been taken to eliminate this information while preserving as much iris information as possible. We propose a novel method to assess the gender information present in the iris more accurately by eliminating gender information in the masks. This consists of pairing iris with similar masks and different gender, generating a paired mask using the OR operator, and applying this mask to the iris. Additionally, we manually fix iris segmentation errors to study their impact on the gender classification. Our results show that occlusion masks can account for 6.92% of the gender classification accuracy on average. Therefore, works aiming to perform gender classification using the iris texture from normalized iris images should eliminate this correlation.

Two-Stage and Two-Discriminator generative adversarial network for the inpainting of irregularly incomplete iris images

Due to the influence of the light source environment during image acquisition or the subject not fully opening their eyes, there are phenomena such as light spot interference, eyelash or eyelid occlusion in the iris area. This will cause the loss of effective iris information, ultimately affecting the success rate of recognition. To address the aforementioned issues, this paper introduces a Two-stage and Two-discriminator Generative Adversarial Network (TT-GAN) to restore irregularly incomplete iris images. The specific process is as follows: Firstly, TT-GAN employs gated convolution in place of ordinary convolution to more effectively extract image information; Secondly, TT-GAN incorporates a PC-Attention attention mechanism within the generator network to enhance the network's restoration capabilities; lastly, to ensure consistency in global and local semantic information of the repaired images, dual SN-PatchGAN discriminators are utilized. The experimental results based on three different spectral iris datasets, IITD, CASIA-Iris-Interval, and UBIRIS.V2, show that the repaired iris images significantly improve objective evaluation indicators compared to the incomplete iris images. Among them, TAR increased by 93.42 %, 68.41 %, and 59.72 %, respectively; EER increased by 27.17 %, 30.18 %, and 23.78 %, respectively; PSNR increased by 17.8712 dB, 18.818 dB, and 24.6053 dB respectively; SSIM increased by 0.0325, 0.0529, and 0.1337 respectively.

Research on Iris Feature Extraction and Recognition Technology Based on Deep Learning

Abstract Certain biological information or behavioral information of a person can achieve the effect of characterizing an individual, and by combining the computer to extract the corresponding information, identity authentication is achieved. In a variety of biometrics, iris relative to fingerprints, handwriting and face, belongs to the structure within the human eye, if you want to steal is very difficult, in order to improve the safety factor, so the iris is used for authentication to achieve iris recognition. This study is based on deep learning iris recognition matching, in order to be able to effectively improve the accuracy of iris recognition, experiments are carried out. Evaluation metrics are performed through Hamming distance to calculate the correct recognition rate to ensure that the iris information can be accurately represented. This study mainly uses the improved PCHIP-LMD algorithm and CNN algorithm, the LMD algorithm is more context-aware, has better generalization ability, flexibility and scalability, while the CNN algorithm has the advantages of local awareness, parameter sharing and automatic parameter learning. In this study, we compare the correct recognition rate of iris recognition between improved PCHIP-LMD and CNN algorithms and get the conclusion that the correct recognition rate of the improved PCHIP-LMD algorithm is only 78%, which is much smaller than that of the CNN algorithm which is 92%, and we get the conclusion that LMD algorithm is suitable for iris recognition with few samples, and it is more suitable to use CNN algorithm when the sample images are too many. CNN algorithm. With the development of technology, the application of iris recognition will be more and more, I believe that soon will be widely popularized in daily life and work.

An effective authentication scheme for a secured IRIS recognition system based on a novel encoding technique

Iris recognition systems are well known for their accuracy than any other biometric system and very popular due to the permanent nature of the iris image. Moreover, it is easy to acquire the iris image even from a very long distance without causing much degradation to the image. In terms of the security perspective, very few biometric systems comprehensively address the vulnerabilities occurring as a result of security attacks. Among the security vulnerabilities, the possibility of compromise of Iris Code during the exchange of Iris information between client and server is a serious threat. In this work, an innovative model which generates a One Time Iris Code (OTIC) for every user authentication is proposed with the help of a novel encoding scheme. In the proposed model, the Iris Code generated distorted using pseudo-random number generator, timestamp, and rotation parameter and it will be different for each authentication session. The proposed technique will also reduce the possibility of compromising the original Iris Code. This will eventually enhance the security of the Iris recognition system. Moreover, the proposed method has less time complexity and high execution speed than the conventional Iris recognition systems. The accuracy of this model is comparable with the existing systems. Various performance analysis methods such as histogram processing for showing the pixel intensity of iris images, and calculation of EER based on entropy measurement for evaluating the severity of attacks using Base Score calculation have been implemented in this work.

Gender Prediction using Deep Learning Algorithms and Model on Images of an Individual

The classification of gender based on the biometric is an old and traditional approach which are treated as a sub-branch of “Soft Computing”. But in this proposed work, works on the celebrity images dataset obtained from the Kaggle repository. The major focus of this to extract the important features from the images by training the neural network by using an autoencoder and decoder. The system extracts the feature maps to visualize the extracted features and makes some sort of interpretations to analyze the data. Gender classification using iris information is a relatively old technique, and most gender classification methods classify all iris texture features. These features are fed to the classifier, this data might be proper or improper, and if it is improper it leads to more generalization results in less accuracy. The proposed algorithm to create increase the size of the dataset implements the Data Augmentation Technique using basic image operations. Then it applies a convolution neural network for predicting the gender of the user. The model has proved practically that increasing the size of the dataset and reducing the features improves the classifiers learning performance.

Ocular recognition databases and competitions: a survey

The use of the iris and periocular region as biometric traits has been extensively investigated, mainly due to the singularity of the iris features and the use of the periocular region when the image resolution is not sufficient to extract iris information. In addition to providing information about an individual's identity, features extracted from these traits can also be explored to obtain other information such as the individual's gender, the influence of drug use, the use of contact lenses, spoofing, among others. This work presents a survey of the databases created for ocular recognition, detailing their protocols and how their images were acquired. We also describe and discuss the most popular ocular recognition competitions (contests), highlighting the submitted algorithms that achieved the best results using only iris trait and also fusing iris and periocular region information. Finally, we describe some relevant works applying deep learning techniques to ocular recognition and point out new challenges and future directions. Considering that there are a large number of ocular databases, and each one is usually designed for a specific problem, we believe this survey can provide a broad overview of the challenges in ocular biometrics.

Keystroke Dynamics-Based Authentication Using Unique Keypad.

Authentication methods using personal identification number (PIN) and unlock patterns are widely used in smartphone user authentication. However, these authentication methods are vulnerable to shoulder-surfing attacks, and PIN authentication, in particular, is poor in terms of security because PINs are short in length with just four to six digits. A wide range of research is currently underway to examine various biometric authentication methods, for example, using the user’s face, fingerprint, or iris information. However, such authentication methods provide PIN-based authentication as a type of backup authentication to prepare for when the maximum set number of authentication failures is exceeded during the authentication process such that the security of biometric authentication equates to the security of PIN-based authentication. In order to overcome this limitation, research has been conducted on keystroke dynamics-based authentication, where users are classified by analyzing their typing patterns while they are entering their PIN. As a result, a wide range of methods for improving the ability to distinguish the normal user from abnormal ones have been proposed, using the typing patterns captured during the user’s PIN input. In this paper, we propose unique keypads that are assigned to and used by only normal users of smartphones to improve the user classification performance capabilities of existing keypads. The proposed keypads are formed by randomly generated numbers based on the Mersenne Twister algorithm. In an attempt to demonstrate the superior classification performance of the proposed unique keypad compared to existing keypads, all tests except for the keypad type were conducted under the same conditions in earlier work, including collection-related features and feature selection methods. Our experimental results show that when the filtering rates are 10%, 20%, 30%, 40%, and 50%, the corresponding equal error rates (EERs) for the proposed keypads are improved by 4.15%, 3.11%, 2.77%, 3.37% and 3.53% on average compared to the classification performance outcomes in earlier work.

Code Based Proficient Transactions Based on QR Code with Biometric Authentication

Now a days the security dangers in the online ex-changes, ATM money withdrawals’ have expanded on a substan-tial scale. Individuals in the country zone are as yet ignorant of these dangers are as yet encountering many loses. Individuals unconscious of these dangers don't perceive that sharing their ATM PIN number, yet additionally sharing their credit/debit card number and CCV can likewise prompt the getting to of financial balance without their insight. In this way, in this proposed framework it is said that the credit/debit card number and CCV are being supplanted with dynamically produced QR codes. This QR code that is created can't be reproduced or hacked as effec-tively as the current framework. In the present framework OTP is sent to authenticate our transactions. This OTP is additionally added with Bio-metric confirmation like unique finger-print, iris information. This provides an additionally improved verification amid all the ATM exchanges. The execution on the above framework is basic as all the financial accounts are now linked with Aadhar card. And this can be implementing the QR code scanner alongside unique finger-print scanner and iris scanner in later stages. This never enables the unapproved clients to access or make any installments without the respective of the account holders. This expands the security level and furthermore enables the client to the exchange cash all the more securely. The pro-posed framework improves the security in the exchanges in our everyday life as no ATM withdrawal or Deposit should be possi-ble without the QR code and the Account holder themselves.

Correlations between serum testosterone and irisin levels in a sample of Egyptian men with metabolic syndrome; (case-control study)

Background Metabolic syndrome (MetS) in men is a common problem. Several studies highlight that testosterone deficiency is associated with metabolic disorders that may be explained by “myokines”. Irisin is an adipo-myokine that has beneficial metabolic effects. Aim To evaluate serum testosterone in a sample of Egyptian men with MetS diagnosed by NCEP ATP III, and correlate it with serum irisin level. Methods A total of 90 men (60 with MetS and 30 healthy age-matched controls) participated in the study. Testosterone level is estimated by an automated system, Irisin level was measured by ELISA. Results Circulating irisin was positively correlated with serum testosterone and was significantly lower in patients with MetS. Conclusion This study highlights the effect of serum testosterone on irisin formation from skeletal muscle. Recommendations; treatments of MetS may include testosterone supplementation. Further studies are needed to elucidate this association.

Integrated Partial Encryption and Watermarking Technique for Digital Video using Chaos and Support Images

Encryption and Watermarking are image processing tools that are used mostly separately. Encrypting a document completely obscures it and watermarking is applied mostly for copy right protection. For entertainment type videos, partial encryption and watermarking may be integrated such that both objectives are attained. We report in this paper a novel combined partial encryption and watermarking algorithm for digital videos using chaotic system and support image. Watermarks are generated by using iris information of the authentic user or the authentic producer of the digital video. Watermarks of full size and half size are embedded in the luminance frame (s) of the video using simple LSB steganography. A sequence of color frames is watermarked in two phases, and partially encrypted in two layers, the watermarks considered are encrypted before embedding. The luminance frame (s) is (are) preprocessed before embedding with the watermarks to defy stega-analysis. Chaotic logistic map and a gray level support digital image is used for obtaining unique row/column sequences as well as for generating robust random binary sequences. A support color digital image is used in the first layer encryption of the digital video. Modified Rabinovich 4 dimensional hyper-chaotic system is used for second layer encryption. Encryption algorithm is applied in R, G, B frames separately. The technique may also be applied for integrated encryption and watermarking of digital images.

An Unsupervised Iris Based Biometric System with Inherent Feature Thresholding

This paper provides a general indication of the existing approaches rely on basic factor (i.e. extraction of iris information, affine transform, and distance matrix) as input. An essential factor in effective design of IRIS based biometric approach is the accuracy with which the model can estimate a region of interest (i.e. IRIS) within constraints and unforeseen issues, which can be very problematical but need of the hour. We introduced a new IRIS based biometric system that incorporates various factors that takes complete information of eye for developing the feature set (digest) while affine transforms are not incorporated while the three sets distance measure is incepted to enhance accuracy. The algorithm-based size of template, functionality of distance measure and/or scope, methods and/or function of application through well-defined scientific and statistical principles. Unfortunately, the accuracy of the existing approaches is limited despite the large scale of experience with several improvements based on digital image processing and statistical models. Henceforth, we incorporated several texture analysis algorithms with computing techniques along with several parametric enhancement constraints to ensure the feasibility, effectiveness and efficiency of the proposed framework in comparison with the existing methods.

Application Design of the Iris Recognition Technology in the Access Control Management System

This design is a method of implementing the iris-recognition-based access control management system, including the identity collection device, database server, infrared range finder, iris recognition device, and access controller. The identity collection device collects the target personnel’s identity signals on site and outputs iris information. When a target person attempts to enter a door, the infrared range finder needs to work with the identity collection device to start the iris recognition device. The identity collection device will promptly and accurately collect the iris information, and then send the extracted iris information to the database server. The database server compares the received iris information with that previously stored in the database and quickly determines whether the iris information is legal based on the comparison results. If it is legal, the access controller sends a signal to the electric control lock to open the door and the person can enter the door. Otherwise, the person cannot enter the door. This design integrates the infrared sensing technology, human-computer interaction technology, data transmission technology, and communication technology. It utilizes the iris recognition algorithm in the iris recognition technology to improve the precision of recognition. Therefore, this design can efficiently identify and authenticate personal identity. In addition, this design features high reliability, low cost, small volume, complete functionality, and strong scalability on information collection.

Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration

Aimed at the one-to-one certification problem of unsteady state iris at different shooting times, a multi-algorithm parallel integration general model structure is proposed in this paper. The iris in the lightweight constrained state affected by defocusing, deflection, and illumination is taken as the research object, the existing algorithms are combined into the model structure effectively, and a one-to-one certification algorithm for lightweight constrained state unsteady iris was designed based on multi-algorithm integration and maximum trusted decision. In this algorithm, a sufficient number of iris internal feature points from the unstable state texture were extracted as effective iris information through the image processing layer composed of various filtering processing algorithms, thereby eliminating defocused interference. In the feature recognition layer, iris deflection interference was excluded by the improved methods of Gabor and Hamming and Haar and BP for the stable features extracted by the image processing layer, and two certification results were obtained by means of parallel recognition. The correct number of certifications for an algorithm under a certain lighting condition were counted. The method with the most correct number was set as the maximum trusted method under this lighting condition, and the results of the maximum trusted method were taken as the final decision, thereby eliminating the effect of illumination. Experiments using the JLU and CASIA iris libraries under the prerequisites in this paper show that the correct recognition rate of the algorithm can reach a high level of 98% or more, indicating that the algorithm can effectively improve the accuracy of the one-to-one certification of lightweight constrained state unsteady iris. Compared with the latest architecture algorithms, such as CNN and deep learning, the proposed algorithm is more suitable for the prerequisites presented in this paper, which has good environmental inclusiveness and can better improve existing traditional algorithms’ effectiveness through the design of a parallel integration model structure.

A Novel Privacy Protection Scheme for Iris Identification

With the development of iris technology, iris identification has been widely used in various domains, as an efficient security authentication pattern. However, as more and more people have been involved in this authentication, they have begun to realize that this pattern may also lead to privacy leakage during the procedure of security authentication. For one thing, the adversary can reverse the iris information of the user with the unauthorized iris template and gets the privacy. For another, the adversary can also get the iris template from other poorly managed application and utilizes this template to access the user’s information in a special application to get the privacy of him. These two types of attack have seriously affected the promotion of iris identification. Thus, in order to cope with above attacks, and based on the conception of random projection and differential privacy protection, this paper proposes a novel privacy protection scheme short for RPDPP to protect the privacy of the user during the procedure of iris identification. In this scheme, a random projection is used in the phase of iris collection and template production, so that the iris template cannot be reversed into the iris information. Then before conserving the real template, several dummy templates that similar with the real iris template have been produced, and these dummies have disturbed the real template to comply with the restriction of ɛ-differential privacy. Thus, the real template of the user is difficult to be identified with other similar dummies, and the adversary cannot get the privacy of the user through multiple queries, even though he had obtained the iris template from other applications. At last, several experiments have been given with different parameters, and the protection ability has been compared with some similar algorithms, and then results of these experiments had further demonstrate the superiority of our proposed scheme.

Relevant features for gender classification in NIR periocular images

Most gender classifications methods from near-infrared (NIR) images have used iris information. Recent work has explored the use of the whole periocular iris region which has surprisingly achieved better results. This suggests the most relevant information for gender classification is not located in the iris as expected. In this work, the authors analyse and demonstrate the location of the most relevant features that describe gender in periocular NIR images and evaluate their influence in classification. Experiments show that the periocular region contains more gender information than the iris region. They extracted several features (intensity, texture, and shape) and classified them according to their relevance using the XgBoost algorithm. Support vector machine and nine ensemble classifiers were used for testing gender accuracy when using the most relevant features. The best classification results were obtained when 4000 features located on the periocular region were used (89.22%). Additional experiments with the full periocular iris images versus the iris-occluded images were performed. The gender classification rates obtained were 84.35 and 85.75%, respectively. From results, they suggest focusing only on the surrounding area of the iris. This allows us to realise a faster classification of gender from NIR periocular images.

Securing one-time password generation using elliptic-curve cryptography with self-portrait photograph for mobile commerce application

Over the decades, the security in mobile commerce application via One Time Password (OTP) plays a vital role. With new developments in mobile wallets, the volume of transactions increase globally and the need for securing transactions is still a challenging aspect. A strong user authentication mechanism is required since many of the attacks take minimum effort to break the security. User Authentication is a critical requirement since many of the attacks take minimum effort to break the security. Hence a novel cryptographic scheme is proposed to bridge the gap between security and authentication mechanisms. Initially, the scheme captures the user image to analyze the face for its structure, texture and feature identification. Once the image components are matched from the available template database user authentication is successful. To avoid conflict during template matching the face components are further subjected to segment location, radius and center of eyeballs at subpixellic accuracy. Iris matching is done via spatial histogram. If the iris information is similar, the Transaction Server (TS) generates OTP and sends it to the user mobile in order to complete the transaction. The proposed model is proven to be highly efficient against False Acceptance Rate (FAR) and False Rejection Rate (FRR) measures and is validated over Q- Fire dataset.

Molecular and Functional Interaction of the Myokine Irisin with Physical Exercise and Alzheimer's Disease.

Irisin, a skeletal muscle-secreted myokine, produced in response to physical exercise, has protective functions in both the central and the peripheral nervous systems, including the regulation of brain-derived neurotrophic factors. In particular, irisin is capable of protecting hippocampus. Since this area is the region of the brain that is most susceptible to Alzheimer’s disease (AD), such beneficial effect may inhibit or delay the emergence of neurodegenerative diseases, including AD. Also, the factors engaged in irisin formation appear to suppress Aβ aggregation, which is the pathological hallmark of AD. This review is based on the hypothesis that irisin produced by physical exercise helps to control AD progression. Herein, we describe the physiology of irisin and its potential role in delaying or preventing AD progression in human.