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Purpose: To investigate the influence of different keratometry (K) measurements on the postoperative outcomes of cataract surgery with extended depth-of-focus (EDOF) intraocular lens (IOL) implantation. Methods: A retrospective cohort study was conducted, and patients who received cataract surgery and one type of EDOF IOL implantation were included. The patients were then categorized according to K measurements, and 70 and 30 eyes were included in the biometric-K and topographic-K groups, respectively. The primary outcomes were postoperative uncorrected distance visual acuity (UDVA), spherical equivalent (SE) and cylinder power. A generalized linear model was applied to compare the adjusted odds ratios (aORs) and 95% confidence intervals (CIs) of the outcomes between groups. Results: One month after surgery, the UDVA was 0.15 and 0.07 in the biometric-K group and topographic-K group, respectively. Furthermore, the final SEs were -0.42 D and -0.13 D in the biometric-K group and topographic-K group, respectively, and the final cylinder powers were -0.35 D and -0.13 D in the biometric-K group and topographic-K group, respectively. According to the multivariate analysis, the topographic-K group presented a significantly better UDVA (P = 0.044) and significantly lower cylinder power (P = 0.031) than the biometric-K group. Angle kappa was significantly correlated with high postoperative astigmatism in the topographic-K group (P = 0.033), whereas angle kappa, steep K, and corneal cylinder powers were significantly correlated with high postoperative astigmatism in the biometric-K group (all P < 0.05). Conclusion: Topography-based K measurements yielded better refractive outcomes than biometric-based K measurements did., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

This paper conducts a comparative analysis of deep models in biometrics using scalogram of electrocardiogram (ECG). A scalogram is the absolute value of the continuous wavelet transform coefficients of a signal. Since biometrics using ECG signals are sensitive to noise, studies have been conducted by transforming signals into a frequency domain that is efficient for analyzing noisy signals. By transforming the signal from the time domain to the frequency domain using the wavelet, the 1-D signal becomes a 2-D matrix, and it could be analyzed at multiresolution. However, this process makes signal analysis morphologically complex. This means that existing simple classifiers could perform poorly. We investigate the possibility of using the scalogram of ECG as input to deep convolutional neural networks of deep learning, which exhibit optimal performance for the classification of morphological imagery. When training data is small or hardware is insufficient for training, transfer learning can be used with pretrained deep models to reduce learning time, and classify it well enough. In this paper, AlexNet, GoogLeNet, and ResNet are considered as deep models of convolutional neural network. The experiments are performed on two databases for performance evaluation. Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, while Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The ResNet was 0.73%-0.27% higher than AlexNet or GoogLeNet on PTB-ECG-and the ResNet was 0.94%-0.12% higher than AlexNet or GoogLeNet on CU-ECG.

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption.

Iris is one of the most widely used biometric modalities because of its uniqueness, high matching performance, and inherently secure nature. Iris segmentation is an essential preliminary step for iris-based biometric authentication. The authentication accuracy is directly connected with the iris segmentation accuracy. In the last few years, deep-learning-based iris segmentation methodologies have increasingly been adopted because of their ability to handle challenging segmentation tasks and their advantages over traditional segmentation techniques. However, the biggest challenge to the biometric community is the scarcity of open-source resources for adoption for application and reproducibility. This review provides a comprehensive examination of available open-source iris segmentation resources, including datasets, algorithms, and tools. In the process, we designed three U-Net and U-Net++ architecture-influenced segmentation algorithms as standard benchmarks, trained them on a large composite dataset (>45K samples), and created 1K manually segmented ground truth masks. Overall, eleven state-of-the-art algorithms were benchmarked against five datasets encompassing multiple sensors, environmental conditions, demography, and illumination. This assessment highlights the strengths, limitations, and practical implications of each method and identifies gaps that future studies should address to improve segmentation accuracy and robustness. To foster future research, all resources developed during this work would be made publicly available. [ABSTRACT FROM AUTHOR]

Research 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. [ABSTRACT FROM AUTHOR]

Next-generation identity verification using biometric features is nearly foolproof with the right classifier. However, selecting the correct classifier poses a key challenge, particularly in the recognition of biometric patterns. High-potential projects may face delays due to a lack of the right recognition mechanism or the malfunction of the selected classifier. This could also result from not choosing the appropriate classifier that aligns with the project's patterns. This study aims to evaluate various classifiers with potential in biometric research and the capabilities of different machine learning algorithms. Several classifiers were experimentally evaluated in combination with dynamic algorithms. The ultimate objective was to identify a standard classifier suitable for general biometric pattern recognition. Using well-known biometric pattern datasets, multivariate algorithms, such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), were applied. These methods were combined with different classifiers, including SVM-L, MLP, KNN, etc. After analyzing the results obtained, the combination of LDA with MLP outperformed other approaches in terms of accuracy. [ABSTRACT FROM AUTHOR]

Contactless fingerprint authentication has gained popularity as a field of research in biometrics in recent years. Unlike traditional fingerprint recognition systems that require direct contact of the person's finger with the sensor, contactless fingerprint systems offer several advantages, among them ease of capture and cost-effectiveness. Despite the progress made in this field, poor contrast, background noise, and limited image information continue to pose difficulties for fingerprint recognition in contactless environments. Furthermore, the number of images in published fingerprint biometric datasets for each person is restricted, and there is insufficient data for efficient training. Nevertheless, Convolutional Neural Network (CNN) architectures have been widely used, necessitating large databases. To address these issues, this paper introduces a Siamese network designed for the purpose of identity Verification, using the contactless thumb fingerprint modality to enhance recognition results. The Siamese network is able to extract pertinent features from noisy images with low contrast and limited information, even if they have limited information and low contrast. Additionally, this work proposes the use of the contactless thumb fingerprint modality instead of the contactless index fingerprint modality, which is more commonly used in related works. Consequently, the Mobile FingerPrint (MFP) dataset is introduced and constructed for evaluation. Experimental results demonstrate the efficiency of the proposed method, achieving an accuracy of 98.68% for thumb fingerprint Verification. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, face biometrics recognition systems are a wide space of a computer usage which is mostly employed for security purpose. The main purpose of the face biometrics recognition system is to authenticate a user from a given database. Due to the widespread expansion of the surveillance cameras and facial recognition technology, a robust face recognition system required. The recognition system needs to store a large number of training samples in any storage unit, that time hackers can access and control that data. So, Protecting and managing sensitive data is essential object. This requires a technique that preserve the privacy of individuals, maintain data integrity, and prevent information leakage. The storage of biometric templates on centralized servers has been associated with potential privacy risks. To address this issue, we have developed and implemented a proof-of-concept facial biometric identification system that uses a private Blockchain platform and smart contract technology. So, the proposed approach is presented a secure and tamper-proof from data breaches as well as hacks with data availability, by using the Blockchain platform to store face images. This paper aims to utilize Blockchain technology to identify individuals based on their biometric traits, specifically facial recognition system makes it tamper-proof (immutable) ensuring security. The system consists of enrolment and authentication phases. Blockchain technology uses peer-to-peer communication, cryptography, consensus processes, and smart contracts to ensure the security. The proposed approach was tested on two popular datasets: CelebFaces Attributes (CelebA) and large-scale face UTKFace datasets. The experimental results indicate that the system yields highly performance outcomes, as evidenced by the Equal Error Rate (EER) values of 0.05% and 0.07% obtained for the CelebA and UTKFace datasets, respectively. The system was compared to three baseline methods and scored the lowest Equal Error Rate. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Manajemen Pendidikan dan Ilmu Sosial (JMPIS) is the property of Dinasti Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The conception of biometric systems as a means of securing sensitive information and enhancing service delivery remains under-researched. To address this knowledge gap, we explore the case of a public-sector social security and pension organisation in Ghana using a qualitative interpretative study approach and the information security model of confidentiality-integrity-availability as an analytical lens. The study's findings indicate that integrating and using biometric identification and authentication as part of delivering social security and pension services can protect availability, confidentiality, and integrity of information. The findings further show that the use of a biometric system for social security and pension information security can contribute to reducing service turnaround time and vulnerability to fraudulent manipulation of benefits payments. The study provides implications for research, practice, and policy. For research, the paper opens up biometric systems' study from the perspective of information security and service improvement. For practice and policy, the study demonstrates the importance of aligning biometric systems' deployment and use with domain application requirements. [ABSTRACT FROM AUTHOR]

The demand for a non-contact biometric approach for candidate identification has grown over the past ten years. Based on the most important biometric application, human gait analysis is a significant research topic in computer vision. Researchers have paid a lot of attention to gait recognition, specifically the identification of people based on their walking patterns, due to its potential to correctly identify people far away. Gait recognition systems have been used in a variety of applications, including security, medical examinations, identity management, and access control. These systems require a complex combination of technical, operational, and definitional considerations. The employment of gait recognition techniques and technologies has produced a number of beneficial and wellliked applications. Thiswork proposes a novel deep learning-based framework for human gait classification in video sequences. This framework'smain challenge is improving the accuracy of accuracy gait classification under varying conditions, such as carrying a bag and changing clothes. The proposed method's first step is selecting two pretrained deep learning models and training from scratch using deep transfer learning. Next, deep models have been trained using static hyperparameters; however, the learning rate is calculated using the particle swarmoptimization (PSO) algorithm. Then, the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization algorithm. This algorithm chooses the best features, combined in a novel correlationbased fusion technique. Finally, the fused best features are categorized using medium, bi-layer, and tri-layered neural networks. On the publicly accessible dataset known as the CASIA-B dataset, the experimental process of the suggested technique was carried out, and an improved accuracy of 94.14% was achieved. The achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this work. [ABSTRACT FROM AUTHOR]

Copyright of Basrah Journal of Science / Magallat Al-Barat Li-L-ulum is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Biometrics refers to the technology for personal recognition or authentication based on our physiological and/or behavioural characteristics. Distinctiveness, Permanence, Collectability, Performance, Acceptability, and Circumvention are the properties of biometric Using only one biometric trait in Authentication systems may not provide all the properties mentioned above. We use more than one biometric trait (similar / different) simultaneously which is called as multi biometric, provides more robust. There are a number of other sources that can be combined to enhance security in the case of combining different biometric modalities. In this paper shows the basic biocrypto system technique used in most of the research. This shows the cryptosystem which stores a key and will be released once the verification is done. Cryptography methods is combined with multibiometric which enhances the security of privacy data. [ABSTRACT FROM AUTHOR]

In order to authenticate remote users for a distributed network-operated system, remote user authentication is crucial. A huge number of enterprise apps run on several servers and a remote user accessing services from several servers needs authentication for most of the cases. In this current research communication a smart card-based remote user authentication in a multi-server environment using user biometrics and a Chebyshev chaotic map is introduced. The server details are hidden from the user, and the user is only given the option of picking his or her service based on its requirements. The server is chosen by the registration center based on the service requested by the user, and a handshaking session is established between the user and the server. Both user and server have to authenticate to the registration center by message passing. Finally, a session key is obtained using this procedure, which is used to accomplish further communication. The proposed solution facilitates modifying passwords and enabling additional services easier. The proposed approach is authenticated using formal security analysis utilizing BAN logic. Security analysis and comparison with some state of the art schemes proves its suitability for multi-server environment. [ABSTRACT FROM AUTHOR]

From border control using fingerprints to law enforcement with video surveillance to self-activating devices via voice identification, biometric data is used in many applications in the contemporary context of a Smart City. Biometric data consists of human characteristics that can identify one person from others. Given the advent of big data and the ability to collect large amounts of data about people, data sources ranging from fingerprints to typing patterns can build an identifying profile of a person. In this article, we examine different types of biometric data used in a smart city based on a framework that differentiates between profile initialization and identification processes. Then, we discuss digital ethics within the usage of biometric data along the lines of data permissibility and renewability. Finally, we provide suggestions for improving biometric data collection and processing in the modern smart city. [ABSTRACT FROM AUTHOR]

The adoption of biomedical signals such as the electrocardiogram (ECG) for biometric is rising in tandem with the increased attention to wearable devices. However, despite its potential benefits, ECG is rarely implemented as a biometric mechanism in real-life wearable applications. Therefore, this research aims to analyse the ECG signals extracted from wearable Hexoskin Proshirt for biometric authentication in different physiological conditions. A total of 11 subjects participated in this study, where the ECG signals were recorded while standing, sitting, walking, and uncontrolled activity. The raw ECG signal is first pre-processed using noise-removal butterworth filters in the time domain, followed by an efficient QRS segmented feature extraction approach. Finally, around 854 datasets were generated for training and validation, while the remaining 300 were used to test the proposed recognition method with a quadratic support vector machine (QSVM). The results show that the proposed method achieved a reliable accuracy above 98% with false acceptance rate (FAR) of 0.93%, false rejection rate (FRR) of 3.64%, and true positive rate (TPR) above 96% on the in-house datasets. This researchs findings confirm the possibility of using ECG biometrics for authentication purposes in various real-life settings with varying physiological parameters using a smart textile shirt. [ABSTRACT FROM AUTHOR]

In recent years, the rise of biometric applications, particularly those centered around iris-based systems, has been significant. High data volumes inherent in these applications and the potential vulnerability of network links necessitate data compression in certain instances. The advantage of lossless compression methods is twofold: they maintain recognition performance without degradation while necessitating fewer computations for differentiation compared to their lossy counterparts. This study proposes a novel approach for lossless/lossy compression of iris biometric sample data across various public iris databases. Initially, the differences between successive images within each class are calculated, leveraging the strong correlation of images within each class. Subsequently, these differences are compressed using quadtree decomposition. This methodology was tested on six renowned iris databases: CASIA V1, CASIA V3, MMU1, MMU2, and UBIRIS Iris, all of which contain 8-bit grayscale images. The results indicate that the proposed strategy offers superior compression performance across different iris databases in comparison to existing methods. Notably, the results suggest that this method can be effectively integrated into an iris biometric recognition system, providing efficient iris image compression, especially when applied in its lossless form. [ABSTRACT FROM AUTHOR]

Glass eel viability determines the success of cultivation and governs recruitment variability, as seeds in aquaculture are recruited to its population. This study aimed to examine the quality of glass eels by using biometric measurements coupled with proximate content. Glass eels were collected monthly using lift-net fishing gear in the Cimandiry Estuary from December 2020 to April 2021. Biometric aspects were examined for length, weight, eye diameter, heart, and liver, whereas proximate analysis was run on proteins, fats, carbohydrates, and ash. The results showed that length and weight steadily increased between months, although the condition factor was indifferent. Eye diameter and mouth opening confirmed the vision of diurnal predatory fish. The heart and liver have demonstrated metabolic capability, and fast-swimming fish favor migratory catadromous fish. Proximate content demonstrated insignificant fluctuations between months and did not confirm the biometric development of the glass eel. However, the glass eels in April were the largest in size, which might be the best for aquaculture and survival in the recruitment process. [ABSTRACT FROM AUTHOR]

This paper presents a systematic literature review on optimizing feature extraction for palm and wrist multimodal biometrics. Identifying informative features across different modalities can be computationally expensive and time-consuming in such complex systems. Optimization techniques can streamline this process, making it more efficient thereby improving accuracy and reliability. The paper frames four research questions on input traits, approaches for feature extraction, classification approaches, and performance metrics of image data. The search query is generated based on the research questions that help retrieve the information on the above parameters. The focus of this paper is to provide the comprehensive and exhaustive gestalt of the appropriate input traits for image data from the information retrieved as well as optimal feature extraction and selection. However, the paper also intends to highlight the various classification approaches taken as well as the performance indicators against those classifiers. Further, the paper aims to analyze the effectiveness of various filtering techniques in eliminating image noise and improving overall system performance using MATLAB 2018. The paper concludes that a combination of palm and wrist biometrics could be a good input-trait combination. This work is novel as it covers multi-faceted processing, addressing various aspects of optimizing feature extraction and selection for palm and wrist multimodal biometrics. [ABSTRACT FROM AUTHOR]

A challenge in social marketing studies is the cognitive biases in consumers' conscious and self-reported responses. To help address this concern, biometric techniques have been developed to obtain data from consumers' implicit and non-verbal responses. A systematic literature review was conducted to explore biometric applications' role in agri-food marketing to provide an integrated overview of this topic. A total of 55 original research articles and four review articles were identified, classified, and reviewed. It was found that there is a steady growth in the number of studies applying biometric approaches, with eye-tracking being the dominant method used to investigate consumers' perceptions in the last decade. Most of the studies reviewed were conducted in Europe or the USA. Other biometric techniques used included facial expressions, heart rate, body temperature, and skin conductance. A wide range of scenarios concerning consumers' purchase and consumption behaviour for agri-food products have been investigated using biometric-based techniques, indicating their broad applicability. Our findings suggest that biometric techniques are expanding for researchers in agri-food marketing, benefiting both academia and industry. [ABSTRACT FROM AUTHOR]

Due of its uniqueness, biometric technologies are employed for security and access control in today's digital world. Global demand for biometric technology has led to the development of biometric systems integrating many features. The robustness depends on the capacity to derive meaningful information from single biometric features. Multimodal biometric security systems are thought to be more accurate and secure than unimodal ones. An attacker may still enter the system using a stolen or hacked biometric data, even the greatest multi-biometric architecture. A hybrid deep learning approach for person identification (HDL-PI) employing palm print, iris, and face biometric features is proposed. It eliminates undesirable artefacts from the raw input image. After feature extraction, we develop a modified group search optimization (MGSO) technique to optimize features and minimize data dimensionality. In order to improve prediction accuracy and minimize error metrics, teacher learning based deep neural networks (TL-DNN) is presented. Performance measurements include accuracy, precision, recall, and F-measure; error metrics include equal error rate (EER), false alarm rate (FAR), false rejection rate (FRR), and genuine acceptance rate (GAR). [ABSTRACT FROM AUTHOR]

The scientific advances of recent years have made available to anyone affordable hardware devices capable of doing something unthinkable until a few years ago, the reading of brain waves. It means that through small wearable devices it is possible to perform an electroencephalography (EEG), albeit with less potential than those offered by high-cost professional devices. Such devices make it possible for researchers a huge number of experiments that were once impossible in many areas due to the high costs of the necessary hardware. Many studies in the literature explore the use of EEG data as a biometric approach for people identification, but, unfortunately, it presents problems mainly related to the difficulty of extracting unique and stable patterns from users, despite the adoption of sophisticated techniques. An approach to face this problem is based on the evoked potentials (EPs), external stimuli applied during the EEG reading, a noninvasive technique used for many years in clinical routine, in combination with other diagnostic tests, to evaluate the electrical activity related to some areas of the brain and spinal cord to diagnose neurological disorders. In consideration of the growing number of works in the literature that combine the EEG and EP approaches for biometric purposes, this work aims to evaluate the practical feasibility of such approaches as reliable biometric instruments for user identification by surveying the state of the art of the last 6 years, also providing an overview of the elements and concepts related to this research area. [ABSTRACT FROM AUTHOR]

In the head-mounted display environment for experiencing metaverse or virtual reality, conventional input devices cannot be used, so a new type of nonintrusive and continuous biometric authentication technology is required. Since the wrist wearable device is equipped with a photoplethysmogram sensor, it is very suitable for use for nonintrusive and continuous biometric authentication purposes. In this study, we propose a one-dimensional Siamese network biometric identification model using a photoplethysmogram. To maintain the unique characteristics of each person and reduce noise in preprocessing, we adopted a multicycle averaging method without using a bandpass or low-pass filter. In addition, to verify the effectiveness of the multicycle averaging method, the number of cycles was changed and the results were compared. Genuine and impostor data were used to verify the biometric identification. We used the one-dimensional Siamese network to verify the similarity between the classes and found that the method with five overlapping cycles was the most effective. Tests were conducted on the overlapping data of five single-cycle signals and excellent identification results were observed, with an AUC score of 0.988 and an accuracy of 0.9723. Thus, the proposed biometric identification model is time-efficient and shows excellent security performance, even in devices with limited computational capabilities, such as wearable devices. Consequently, our proposed method has the following advantages compared with previous works. First, the effect of noise reduction and information preservation through multicycle averaging was experimentally verified by varying the number of photoplethysmogram cycles. Second, by analyzing authentication performance through genuine and impostor matching analysis based on a one-dimensional Siamese network, the accuracy that is not affected by the number of enrolled subjects was derived. [ABSTRACT FROM AUTHOR]

A new biometric identity authentication scheme is proposed based on fuzzy extractor and the advantages of blockchain with decentralization and anonymity. First of all, the fuzzy extractor for biometric information is used to participate in the authentication process, which solves the problem of permanent unavailability caused by the leakage of biometric template. Then, the Fabric architecture is used to build a blockchain to store the hash value of the random key obtained through fuzzy extractor, and it can solve the problem of the centralized storage existed in the traditional identity authentication mechanism. Based on blockchain and fuzzy extractor, a two-factor identity authentication scheme is realized. We performed experimental simulations on our proposed algorithm and the security of our scheme has been shown by analyzing the simulated enemy attack and resistance under some extreme circumstances. Meanwhile, our efficiency analysis also shows the availability of our scheme. [ABSTRACT FROM AUTHOR]

In the present digital era, the number of ways to misuse multimedia data increases day by day. The availability of high image editing tools can change the image so it becomes challenging to predict the data. Watermarking is one of the techniques which can protect the identity of users. This is a highly secured method to protect digital images. In the presented paper, we suggested a more robust image watermarking approach using DWT-SVD with Chaotic encryption. This work mainly focuses on the protection of biometric images. The entropy map (blocks of 4 × 4) is estimated to apply for 2D discrete wavelet transformation (DWT). The LL band is considered for the watermark embedding process because of holding more information about the original image. Further, an orthogonal matrix is obtained using (Singular value decomposition) SVD and edge entropy-based thresholding is used. The chaotic encryption is performed over a watermark image. The encrypted form of copyright image is used to embed into that orthogonal matrix for performing watermarking. The results evaluated using fingerprints and gait biometrics datasets show the robustness of the proposed approach. The performance of the technique is evaluated using BER, PSNR and NC. The watermark is less fragile after testing under various noise and geometrical attacks. The technique's performance is also compared with recent techniques in this domain, where the proposed method outperforms. [ABSTRACT FROM AUTHOR]

Introduction of large training datasets was essential for the recent advancement and success of deep learning methods. Due to the difficulties related to biometric data collection, facial image datasets with biometric trait labels are scarce and usually limited in terms of size and sample diversity. Web-scraping approaches for automatic data collection can produce large amounts of weakly labeled and noisy data. This work is focused on picking out the bad apples from web-scraped facial datasets by automatically removing erroneous samples that impair their usability. The unsupervised facial biometric data filtering method presented in this work greatly reduces label noise levels in web-scraped facial biometric data. Experiments on two large state-of-the-art web-scraped datasets demonstrate the effectiveness of the proposed method with respect to real and apparent age estimation based on five different age estimation methods. Furthermore, we apply the proposed method, together with a newly devised strategy for merging multiple datasets, to data collected from three major web-based data sources (i.e., IMDb, Wikipedia, Google) and derive the new Biometrically Filtered Famous Figure Dataset or B3FD. The proposed dataset, which is made publicly available, enables considerable performance gains for all tested age estimation methods and age estimation tasks. This work highlights the importance of training data quality compared to data quantity and selection of the estimation method. [ABSTRACT FROM AUTHOR]

The paper shares the experience, challenges and efforts to introduce expedited pre-boarding passenger at Narita Airport through digital technology under the impact of COVID-19. The digitalised 'Fast Travel' processing enables more stringent ID checks with consistency, improved efficiency and customer experience using biometric and automation technology to achieve the following improvements in customer experience triggered by a survey conducted by the International Air Transport Association (IATA). The paper also covers pending issues, such as the lack of stress testing and identification of issues that require improvement to serve a larger mass traffic volume as cross-border travel restarts around the world. It is hoped that the contents of the paper may contribute to inspire airports and stakeholders around the world to jointly establish a digitalised pre-boarding procedure that is globally interoperable with a standardised processing and synchronisation of datasets that is protected by a holistic security framework. [ABSTRACT FROM AUTHOR]

The Electrocardiogram records the heart's electrical signals. It is a practice; a painless diagnostic procedure used to rapidly diagnose and monitor heart problems. The ECG is an easy, noninvasive method for diagnosing various common heart conditions. Due to its unique advantages that other humans do not share, in addition to the fact that the heart's electrical activity may be easily detected from the body's surface, security is another area of concern. On this basis, it has become apparent that there are essential steps of pre-processing to deal with data of an electrical nature, signals, and prepare them for use in Biometric systems. Since it depends on the structure and function of the heart, it can be utilized as a biometric attribute for identification and recognition. This academic paper presents a new data formatting technique to be used in biometric systems based on the two major dataset kinds, PTB and PTB-XL. [ABSTRACT FROM AUTHOR]

Remote user authentication schemes provide a system to verify the legitimacy of remote users' authentication request over insecure communication channel. In the past years, many authentication schemes using password and smart card have been proposed. However, password might be guessed, leaked or forgotten and smart card might be shared, lost or stolen. In contrast, the biometrics which utilize biological characteristics, such as face, fingerprint or iris, have no such weakness. With the trend of mobile payment, more and more applications of mobile payment use biometrics to replace password and smart card. In this paper, we propose a biometric-based remote authentication scheme substituting biometric and mobile device bounded by user for password and smart card. This scheme is more convenient, suitable and securer than the schemes using smart cards on mobile payment environment. [ABSTRACT FROM AUTHOR]

The study of chaotic constructions and their associated cryptographic frameworks has sparked a lot of research interest in recent years. Latest advances in wireless technology and the proliferating limitations posed by open communication channels, biometric-enabled remote client authentication procedures with passwords have recently gained traction. In order to address this problem, this paper proposes a secure biometric-based remote user authentication procedure using convolution-Chebyshev chaotic maps with a key agreement procedure. The extended convolution-Chebyshev chaotic maps-based scheme was developed over the interval (- ∞ , + ∞) , and the required properties for the procedure were verified rigorously. The proposed procedure provides a secure client authentication mechanism using biometrics. Additionally, the projected procedure provides a good key agreement feature with perfect forward secrecy while reducing the computation loads for smart cards. As a result, the proposed procedure outperforms related authentication procedures in terms of security and computational performance. [ABSTRACT FROM AUTHOR]

In machine learning, an efficient classifier model design is mostly based on effective feature extraction and appropriate feature selection. This work mainly focused on different optimized feature selection algorithms for automatic biometric recognition system with the use of electrocardiogram (ECG) signals. Initially, the features are extracted from P-QRS-T segments of the ECG signal with position normalization. The extracted features are processed through different optimization algorithms for quality assessment prior to the classification stage. In this work, two different methods are proposed based on wrapper feature selection and embedded feature selection to get optimized feature datasets for the perfect identification of human biometrics. In the first proposed method, wrapper-oriented feature selection is implemented with genetic algorithm (GA) and particle swarm optimization. In the second proposed method, the embedded method is included as the least absolute shrinkage selection operator and elastic net (EN). The processed optimized feature vectors from the optimization phase are fed to popular machine learning techniques, such as support vector machine and random forest (RF) classifiers, for automatic biometric human recognition. Classifier performances are investigated using two publicly available open-source databases, Electrocardiogram identification and large Physikalisch-Technische Bundesanstalt diagnostic database with a dynamic number of subjects. The experimental results indicate that the proposed combination of feature selection with machine learning algorithms has enhanced the classification accuracy. Finally, GA and EN with RF classifier provide improved recognition rates of 95.30% and 94.90%, respectively. [ABSTRACT FROM AUTHOR]

Human recognition systems based on biometrics are much in demand due to increasing concerns of security and privacy. The human ear is unique and useful for recognition. It offers numerous advantages over popular biometrics traits face, iris, and fingerprints. A lot of work has been attributed to ear biometric, and the existing methods have achieved remarkable success over constrained databases. However, in unconstrained environment, a significant level of difficulty is observed as the images experience various challenges. In this paper, we first have provided a comprehensive survey on ear biometric using a novel taxonomy. The survey includes in-depth details of databases, performance evaluation parameters, and existing approaches. We have introduced a new database, NITJEW, for evaluation of unconstrained ear detection and recognition. A modified deep learning models Faster-RCNN and VGG-19 are used for ear detection and ear recognition tasks, respectively. The benchmark comparative assessment of our database is performed with six existing popular databases. Lastly, we have provided insight into open-ended research problems worth examining in the near future. We hope that our work will be a stepping stone for new researchers in ear biometrics and helpful for further development. [ABSTRACT FROM AUTHOR]

Spoofing attacks on the fingerprint scanners become major and serious concern with the growing development and use of biometric technologies. Level 1 and Level 2 features; which are said to be unique and most commonly used features in fingerprint verification systems, are easily get spoofed. Moreover, single feature based specifically designed spoof detection methods are not performed well on different fingerprint scanners and spoofing materials. This paper proposed the fusion of pores perspiration and texture features in static software based approach to identify live and fake fingerprints. The pores perspiration activity is quantified by computing the ridge signal energy and gray level distributions around the detected pores. These pore characteristics are statically determined instead of dynamic measurement. Autoencoder neural network is used to reduce the high dimensional feature vector and learn its low dimensional hidden representation unsupervisedly. The binary classification in two classes: live and spoof is performed by the supervisedly trained softmax classifier. The performance of the classifier is evaluated in terms of Average Classification Error (ACE) and misclassification rates: FerrLive and FerrFake. The experimental results carried out on LivDet 2013 and LivDet 2015 databases show the improvement of the classifier performance in comparison to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In order to solve the problems of few and complex traditional cross domain authentication schemes, a double-offloading biometric authentication for cross domain scheme based on blockchain technology was proposed. Firstly, we use fuzzy extraction technology to extract the random key of biometric authentication, so the problem of the biometrics leakage caused by the permanent unavailability was solved. Secondly, we use the untamable blockchain to store biometric public information, and the threat of the fuzzy extraction technology which is vulnerable to be active attacked was solved. Finally, based on the distributed storage function and consortium blockchain architecture, we achieve the biometrics cross domain authentication of user in local and remote environment. In addition, for double-offloading, one of them is when the scheme offloads the server of the user's registered domain, which is no longer needed after helping the user register and submit information to the blockchain service node. The other offloading is the blockchain service node can be offloaded when the other domain server gets and stores the authenticated information of the client. The security and efficiency analysis show that our scheme is not only secure but also practical. Finally, we give the security proof and the efficiency analysis of our proposed scheme. [ABSTRACT FROM AUTHOR]

Authentication systems employ various access mechanisms for the data validation process. Nowadays there are many proposals focused on solving the traditional problems that exist to validate the records of installations in public or private places. With the appearance of COVID-19, the use of technology has intensified to avoid contact with physical devices and achieve successful access. This article shows a compilation of work dedicated to methods and techniques for access to facilities by reading modern biometric systems. [ABSTRACT FROM AUTHOR]

Smart grid (SG) provides a suitable adjustment in the amount of power generation by providing the ability to supervise consumer behavior. SG uses in the smart system to encourage cultural heritage because it is accountable for providing power without any interruption. SG is one of the vital components to authorize smart systems with a lot of smart features to attract visitors to come and visit heritage. In SG, environment security and privacy are the major concern for communications. An authentication protocol provides secure communication between users and service provider for security and privacy purpose. Several authentication protocols are available in the literature. However, they are enabled to known security attacks easily or they are not computationally efficient for SG communication. In the present paper, we design an ECC-based mutual authentication protocol for smart grid communication using biometric approach. The present framework satisfy various security features such as replay attack, user anonymity, man in the middle attack, key freshness, message authentication, session key agreement, impersonation attack, non-traceability and non-transferability. Further, the proposed protocol takes much less communication and computation costs compared with other existing protocols in SG environment. Therefore, our scheme is convenient for practical application in SG communication. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Owing to the recent fuel in investigation of lip as a biometrics trait both in physiological and behavioral aspect, and in healthcare application, it is imperative to comprehensively study and document the literature. Hence, in this work we enlist the works related to the lip as a biometirc trait and other applications-based on its behavior and physiology. Consequently, in this survey we first discuss the lip anatomy that permits to identify the physiological and behavioral properties. Followed by the list of challenges to characterize lip behavior and physiology. Next, we proceed to provide an insight of several inspiring ideas proposed in the literature to establish lip behavior and physiology for identity and other applications such as health monitoring and forensic. Whilst, it can be concluded at the end of the survey that their are several open research area and unsolved issues, which we discuss in details at the end of this survey to attract the attention of researchers. [ABSTRACT FROM AUTHOR]

This research was conducted with the broad objective of providing solution to the traditional student attendance system to a fullycomputerized and automated system. The system was designed to improve the existing method of attendance taking by introducing a biometric feature i.e., fingerprint capturing. The system was built using C# and SecuGen fingerprint reader. It has successfully been able to capture the fingerprint image of any individual within a time range of 0.2 to 0.5 seconds, differentiating each captured fingerprint from others which has been captured previously, and recording the input data, there by achieving the objectives of thestudy. This system has been able to circumvent the problem of drudgery and in efficiency of the conventional method of attendance taking, and it is therefore recommended that this application of biometric system be adopted and used in schools and organizational bodies to reduce impersonation that is common in the old system of verifying candidates. [ABSTRACT FROM AUTHOR]

The face represents a unique part of the human body as well as a biometric identifier. Using facial features, we can achieve create and use applications or systems based on facial recognition. This project involves the construction of such a system which will use facial features to mark the presence, entry time, and finally the creation of an extracted document in the Excel application. The project was developed by researching and using libraries, models as well as Machine Learning based algorithms that are necessary for face detection, training, and recognition during system operation. Python programming language is used to create the system, and CSS is used to design interfaces. The system is built in the form that professors have access to the registration of new students, as well as the creation of relevant courses and obtaining the final attendance report after receiving notes from the system. [ABSTRACT FROM AUTHOR]

Nowadays, person recognition has received significant attention due to broad applications in the security system. However, most person recognition systems are implemented based on unimodal biometrics such as face recognition or voice recognition. Biometric systems that adopted unimodal have limitations, mainly when the data contains outliers and corrupted datasets. Multimodal biometric systems grab researchers' consideration due to their superiority, such as better security than the unimodal biometric system and outstanding recognition efficiency. Therefore, the multimodal biometric system based on face and fingerprint recognition is developed in this paper. First, the multimodal biometric person recognition system is developed based on Convolutional Neural Network (CNN) and ORB (Oriented FAST and Rotated BRIEF) algorithm. Next, two features are fused by using match score level fusion based on Weighted Sum-Rule. The verification process is matched if the fusion score is greater than the pre-set threshold t. The algorithm is extensively evaluated on UCI Machine Learning Repository Database datasets, including one real dataset with state-of-the-art approaches. The proposed method achieves a promising result in the person recognition system. [ABSTRACT FROM AUTHOR]

Remote user authentication is an efficient technique to perform secure communication over an insecure network.In this current research article, a lightweight remote user authentication scheme using smart card is proposed. The proposed scheme is a multi-server authentication mechanism where user biometric plays a prominent role. Here, the user is provided the freedom of selecting his or her service according to its requirement. Registration center selects the server according to the service selected by the user and provides a handshaking between user and server. Both user and server have to mutually authenticate each other to an authorized registration center by message passing. By this technique, finally a session key is generated which is used to perform further communication. Security analysis of the proposed method proves its robustness and comparative analysis with some existing schemes provides the superiority. [ABSTRACT FROM AUTHOR]

Information is a key asset of an organization. In order to maintain the security of a system, biometric authentication is useful in various applications such as online banking, aadhaar linking, and immigration control, e-voting. The main objective of this paper is to analyze the vascular pattern-based finger vein biometric as a promising solution for user authentication, in this globally connected digital world. In this paper, we discussed different user authentication mechanisms, authentication factors and some significant tools available for authentication. It also provides the review of processing stages of finger vein authentication such as image acquisition, image preprocessing, image enhancement, feature extraction and matching. It also gives detailed information related to publicly available finger vein databases developed in different universities for research work. In near future, due to the development of new technologies finger vein will become the most commonly used authentication method. [ABSTRACT FROM AUTHOR]

The use of biometric data (BD) records promises to advance the software engineering field. The rapid adoption of wearable computing technology has widely increased the amount of BD records available. Several aspects about the use of BD records in software engineering field are unknown, such as body measurements used to support daily tasks, and empirical methods that are used to evaluate their benefits. Consequently, a thorough understanding of state-of-the-art techniques still remains limited. This article, therefore, aims at providing a classification and a thematic analysis of studies on the use of BD records in the context of software development. Moreover, it seeks to introduce a classification taxonomy, and pinpoints research gaps, challenges and trends. A systematic mapping of the literature was designed and performed based on well-established practical guidelines. In total, 40 primary studies were analysed and categorised, which were selected by applying a careful filtering process from a sample of 3930 studies to answer seven research questions. Over 77% of articles use more than one biometric aspect to analyse tasks performed by developers; over 47% of articles used eye-track sensor to analyse biometric factors, followed by brain-wearable sensors with 40%, skin sensor with 22%, cardiac sensor with 20%, and others fewer representatives; most studies analysed had their quality assessed as high; most studies were published in journal. This study provides a systematic map of studies that use BD records in software engineering, thereby serving as a basis for future research. [ABSTRACT FROM AUTHOR]

Dynamic student's attention monitoring in the class rooms, online study is essential. Existing model fail to predict the student attention, understanding, engagement automatically using biometric features. Current traditional system is manual with less efficiency and time consuming. Even in both class room based traditional education, e-learning systems, dynamic learner behaviour is not considered. To address this problem statement, this survey paper explores how dynamic learner behaviour of audience, students can be obtained using computer vision and machine learning techniques to predict the engagement level. This research paper also focus on an image processing with deep learning process of invoking, extracting biometric features from the students and detect students attention, distraction towards the class and based on the result outcome the proposed system recommends theory and video based content to obtain the attention back. For deep learning model, we use Convolutional Neural Network (CNN) for training the input models. [ABSTRACT FROM AUTHOR]

The pattern detection device for biometric identification, which is discussed in the following paper, made use of mathematical modelling and descriptive statistics together with exploratory factor analysis i.e., Principal Component Analysis on the point of a function extraction technique. The proposed prestige gadget consisted of androgenic hair for the identification of biometric features with four hundred photographs for each database. A total of four-hundred pictures were gathered from each database. It was taken from a total of 25 respondents and sixteen snapshots from each respondent from hospitality industry. Performed with the highest accuracy, the system utilized a histogram equation with 2-fold cross-recognition, seventy-six. 68% of the average precision for the facial database and 19% mean accuracy for the androgenic hair database. Both means of accuracy are achieved using the 90 maximum large eigenvalues and their complementary eigenvectors within the principal component analysis attribute extraction technique. [ABSTRACT FROM AUTHOR]

Every tertiary academic institution has the obligation of recording and taking the attendance of its students during lecture periods. Over the years the process and accuracy of this attendance has been marred by many challenges which range from the cumbersome nature of paper sheets used in recording, to the manipulation of attendance by unscrupulous students and so on. This paper is aimed at designing and implementing a biometric students' time and attendance logging system that helps in mitigating these challenges. This biometric student time and attendance logging system uses the fingerprint biometric characteristics to accurately identify the student. The system also generates a report showing the number of times a particular student was present or absent in a semester. This feat was achieved through the use of an atmega328P microcontroller, a fingerprint RS305 module, a liquid crystal display (LCD), a Red Green Blue (RGB) light emitting diode (LED) and a Personal Computer (PC) amongst others. The software used in this work was developed using visual studio C# and MySQL2008, and the final work was simulated using proteus before it was constructed and tested. The results of the test show a significant improvement over the manual attendance system. [ABSTRACT FROM AUTHOR]

Some sexually motivated serial killers target victims on the basis of appearance. Therefore, multiple victims of a single serial killer are likely to have some facial features and geometries that are similar. The current research was undertaken to propose a technique, termed face similarity linkage, to evaluate whether victims of a serial killer have statistically more similar facial measurements than a randomly chosen person of the same gender. To test this, three of Ted Bundy's victims were randomly selected and anatomical landmarks were located and measured to produce proportionality indices of their faces. A random subject from an online database was used as a comparison. The results showed there were no statistically significant differences between the three of Bundy's victims, however there was significant difference between 11 of the 17 facial measurements of Bundy's victims when compared to a random person. This research serves as a proof of concept that, with more advanced means of data collection, may be a useful tool for law enforcement for linking serial homicides. The current method is relatively novel, and in need of expert systems interfaces to improve speed and application, which is outlined in the current study. [ABSTRACT FROM AUTHOR]

In post-quantum cryptography, there are security flaws in the key exchange protocol that are easy to lose passwords and difficult to achieve mutual authentication. This paper proposed a new biometric authentication key exchange protocol based on ring learning with error. The constructed cryptosystem according to the RLWE problem had the advantages of short key and ciphertext size, and high operational efficiency. It used biometrics and passwords as long-term keys. The Peikert error coordination mechanism extracted random and uniform session keys from their respective ring elements, which realized explicit authentication of servers to customers. The performance analysis results show that the scheme can resist user fake attacks, have higher security attributes and improve communication efficiency. [ABSTRACT FROM AUTHOR]