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Currently, security enhancement of biometric systems is an important issue that deserves consideration. This is attributed to the threats facing traditional recognition systems, which depend on Personal Identification Numbers (PINs) that can be stolen, easily. Utilization of original biometrics to access user services may lead to loss of the biometrics forever, if hacking attempts succeed in gaining access to the storage database of original templates. To address this concern and to avoid the utilization of original biometrics, we keep them away from being compromised through the utilization of cancelable biometric templates. This paper introduces a novel methodology for user authentication with multiple biometrics to generate distorted non-invertible cancelable templates to be stored in the database. The proposed framework begins with Discrete Cosine Transform (DCT) to achieve data compression in a multi-biometric scenario. After that, Double Random Phase Encoding (DRPE) is applied to increase the security level of the generated templates. Finally, an adaptive filter is used to induce an effect of whitening to generate the cancelable biometric templates. The generated patterns are uncorrelated due to the effect of encryption and adaptive filtering, which improves the security level against identity theft and provides good performance. Simulation results prove a good performance of the proposed cancelable biometric recognition framework with an Area under the Receiver Operating Characteristic curve (AROC) of 52.12% and an Equal Error Rate (EER) of 44.8462%. [ABSTRACT FROM AUTHOR]

Cancellable biometrics is an important trend in biometric security systems, allowing for intentional distortions or variations in the original biometric data to be used for authentication, while preserving privacy of users and security of the original data. This paper presents two cancellable biometric recognition algorithms based on quaternion mathematics. The first algorithm depends on the Fractional Fourier transform (FRFT) with quaternion mathematics to induce intentional distortions in biometric data, while the second algorithm relies on quaternion rotation to achieve the desired level of distortion. Both algorithms are evaluated using Equal Error Rate (EER) and Area under Receiver Operating Characteristic curve (AROC). The simulation results indicate that both algorithms achieve EER values close to 0 and AROC values close to 1, demonstrating their effectiveness and reliability for cancellable biometric recognition. The proposed cancellable biometric recognition algorithms are intended to preserve privacy of users and security of biometric data, while maintaining high levels of accuracy and performance. The use of quaternion mathematics allows for intentional distortions to be introduced in a controlled and secure manner, ensuring that the original biometric data remains protected from either hacking or unauthorized access. In conclusion, the proposed cancellable biometric recognition algorithms based on quaternion mathematics are reliable and effective solutions for biometric security, providing both security and privacy preservation within the biometric authentication process. [ABSTRACT FROM AUTHOR]

Introduction Modern warfare operations are volatile, highly complex environments, placing immense physiological, psychological, and cognitive demands on the warfighter. To maximize cognitive performance and warfighter resilience and readiness, training must address psychological stress to enhance performance. Resilience in the face of adversity is fundamentally rooted in an individual's psychophysiological stress response and optimized through decreased susceptibility to the negative impact of trauma exposure. The current project aims to optimize warfighter expertise, resilience, adaptability, and performance by utilizing a validated Full Dive Virtual Reality (FDVR) training platform to provide high-fidelity, safe, and scalable, controlled stress exposure in highly realistic simulated training scenarios with the most advanced, immersive technology available. Materials and Methods Following Institutional Review Board approval and consent, 2 operators were fitted with high-fidelity virtual reality headsets with hand and eye tracking, full-body haptic feedback suits, a 360° omnidirectional treadmill, and Food and Drug Administration (FDA) cleared biometric monitors. Following acclimation, operators were placed in an industrial fire scenario and instructed to respond as a firefighter and paramedic, to search for and resuscitate any casualties, extinguish the fire, and exfiltrate safely. Following initial acclimation and after each demonstration (n  = 2), 3 semistructured interviews asked operators their perceptions and experiences related to FDVR, focusing on usability, feasibility, and safety. Biometric data were continuously recorded using the Caretaker Medical VitalStream. Results Proof-of-concept (POC) testing proved that the FDVR training platform is usable, safe, and feasible. It creates an immersive environment with physiological responses to mimic realistic Mass Casualty Events (MCEs). Using a case study approach, transcript data were analyzed using thematic analysis. Three major themes emerged: Sensory deficits reduced realism, but sensory feedback improved fidelity, vestibular discord affected the virtual reality experience but only when the system did not respond naturally to operator movement after acclimation, and movement accommodations were made by operators to enhance usability, especially for fine motor movements. Biometric data analysis correlated timestamps from the VitalStream unit with operator responses to stress-inducing events (i.e. explosions, fires, and a deceased victim). Both operators exhibited significant physiological responses, including elevated heart rate, systolic blood pressure, and mean arterial pressure, particularly following explosions, encountering fire, and encountering the deceased victim within the training environment. Conclusions The FDVR training platform overcomes the obstacles of in-person simulation training and provides the closest to real-life experience available. It will allow warfighters to train with their teams in immersive environments that replicate the conditions in which they are expected to perform their duties. The POC demonstrated that physiological responses can be mapped to scenario events to allow tracking of stress responses, cognitive load, as well as performance, and decision-making of the warfighter. The POC only involved 2 operators, but served to prove that the platform was safe and effective. Future testing plans to include 200 warfighters in operational teams of 10 to 12 to further validate the training effectiveness of the FDVR platform. [ABSTRACT FROM AUTHOR]

The buzzword "smart borders" captures the latest instantiation of media technologies constituting state bordering. This article traces historical techniques of knowledge-production and decision-making at the border, in the case of Ellis Island immigration station, New York City (1892–1954). State bordering has long been enabled by media technologies, engulfed with imaginaries of neutral, unambiguous, efficient sorting between desired and undesired migrants—promises central to today's "smart border" projects. Specifically, the use of "proxies" for decision-making is traced historically, for example, biometric or biographic data, collected as seemingly authentic and neutral stand-ins for the migrant. Techniques of selecting, storing, and correlating proxies through media technologies demonstrate how public health anxieties, eugenics, and scientific technocracy of the Progressive Era formed the context of proxies being entrusted to enable decision-making. This pre-digital history of automation reveals how the logics and politics of proxification endure in contemporary border regimes and automated media at large. [ABSTRACT FROM AUTHOR]

This paper proposed a noble face recognition and biometric encryption technique for the biometric passport by applying encrypted biometric data encoded into the High-Capacity Color Two-Dimensional (HCC2D) Code to secure the biometric data. The proposed biometric encryption method is attained by applying the Secure Force (SF) algorithm and encoding it into the HCC2D code. The face recognition technology is achieved by combining the Viola-Jones (VJ) algorithm and the Local Binary Patterns (LBP) algorithm to extract the facial features and generate the template. This method will ensure the safety and security of a biometric passport from unauthorized access without knowing the passport holder. The proposed method will provide more security for border crossing and illegal immigrants for national security. [ABSTRACT FROM AUTHOR]

The advent of the metaverse has given rise to metacrime, a novel category of criminal activities occurring in the metaverse, which not only challenges conventional digital criminality but existing law enforcement frameworks. To address the scholarship vacancy, this study examines the intersections and distinctions between metacrime and conventional cybercrime by employing a multi-disciplinary literature review and comparative analysis. We identified five shared characteristics between these two crime types: crime classification, continuous evolution, hyper-spatial-temporality (global reach), anonymity, and governance challenges. Crucially, our research highlights the distinct epistemological aspects of metacrime through its criminogenic, victimogenic, etiological, ethical, and regulatory dimensions, exemplified by virtual-to-physical attacks, immersive virtual reality attacks, victimization superrealism, complexities of human-avatar interactivity, excessive misuse of biometric data, increasingly vulnerable populations, and avatars' liability. Our findings underscore the imperative need for tailored and forward-thinking regulatory responses to address the intricate challenges of metacrime, thereby ensuring the security and integrity of evolving digital environments. [ABSTRACT FROM AUTHOR]

Fingerprint recognition technology is widely employed for identity verification and access control across diverse domains in which the quality of fingerprint images is critical for accurate biometric identification. However, fingerprint images can be damaged or incomplete due to various factors such as sensor limitations, environmental conditions, or physical injuries. To address this, several approaches including attention mechanism based autoencoders, multi-kernel sequential and multi-kernel stacked autoencoders, and multi-kernel ensemble autoencoders are proposed to perform reconstruction of incomplete and damaged fingerprints. Attention mechanisms play a crucial role by selectively emphasizing important regions and details, further enhancing information capture. By utilizing different autoencoder models with varying kernel sizes, local details and global context can be effectively captured, resulting in more precise restoration. The adoption of multi-kernel sequential and multi-kernel stacked autoencoders enables the extraction of increasingly abstract features, enhancing the model's ability to capture complex fingerprint characteristics. Utilizing autoencoder-based methods for the reconstruction of damaged or incomplete fingerprint images can enhance the accuracy and reliability of fingerprint identification systems leading to improved security and efficiency in real-world applications. Evaluation is conducted using image quality assessment metrics and feature-based matching is utilized to compute fingerprint matching accuracy. The multi-kernel ensemble autoencoder model produces the best reconstruction output with an average fingerprint matching accuracy of 93.81 %. The findings highlight the effectiveness of the proposed work in achieving high-quality reconstruction output and accurate fingerprint matching. The proposed work can be applied in various domains that include law enforcement, security, forensic analysis, and biometric authentication. [ABSTRACT FROM AUTHOR]

The Regulation (EU) 2016/679 on the protection of natural persons regarding the processing of personal data (GDPR) is one of the key fundamental pieces of European legislation to protect human rights and freedoms. However, the development of AI systems that are capable of collecting and processing large amounts of data and predicting user habits and emotional states has affected traditional legal categories and tested their resilience. This paper assesses the limits of the current formulation of the GDPR which does not take expressly into account the category of inferred data as a special category of data. Furthermore, it questions whether the toolbox put in place by the GDPR is still effective in protecting data subjects from practices such as neuromarketing and eye-tracking systems. It shows that it is certainly the essential starting point, but that, on the other hand, cannot be spared criticism. For this, in the recent years, the European legislator has adopted further legislations including, in particular, the Digital Services Act (DSA) and the Artificial Intelligence Act (AIA). Although representing a step forward in protection against such technologies, they each have critical aspects that need to be considered. [ABSTRACT FROM AUTHOR]

This study aimed at developing a noncontact authentication system using event-related pupillary response (ErPR) epochs in an augmented reality (AR) environment. Thirty participants were shown in a rapid serial visual presentation consisting of familiar and unknown human photographs. ErPR was compared with event-related potential (ERP). ERP and ErPR amplitudes for familiar faces were significantly larger compared with those for stranger faces. The ERP-based authentication system exhibited perfect accuracy using a linear support vector machine classifier. A quadratic discriminant analysis classifier trained using ErPR features achieved high accuracy (97%) and low false acceptance (0.03) and false rejection (0.03) rates. The correlation coefficients between ERP and ErPR amplitudes were 0.452-0.829, and the corresponding Bland--Altman plots showed a fairly good agreement between them. The ErPR-based authentication system allows noncontact authentication of persons without the burden of sensor attachment via low-cost, noninvasive, and easily implemented technology in an AR environment. [ABSTRACT FROM AUTHOR]

The biometric authentication in today’s digital era has become a vital issue in security and privacy. Biometrics is currently becoming more significant specifically for higher security modules. Here, a deep feed-forward neural network-based biometric authentication system (DFFNN_biometric authentication system) is presented for biometric authentication utilizing a biometric fingerprint image. In blockchain network, biometric data are considered and the fingerprint images are fed as input. As an input fingerprint image, minutiae extraction is accomplished. Thereafter, a deep key is generated employing a deep residual network (DRN) for template protection. Thereafter, privacy protection template generation is conducted and the templates thus obtained are stored on a database (DB). On the other hand, a query fingerprint image is given as input in the authentication stage. The processes such as minutiae extraction, deep key generation using DRN and privacy protection template authentication are carried out as like it performed in the blockchain network. Additionally, the matching process is conducted between the output acquired in the authentication phase and image stored in DB. The reputation score is generated from authenticated biometric behavior. The DFFNN is utilized to generate a reputation score. Moreover, the DFFNN_biometric authentication system obtained a maximum accuracy of 90.8%, maximum genuine accept rate (GAR) of 0.936, minimum false acceptance rate (FAR) of 0.588, minimum false rejection rate (FRR) of 0.556 and maximum reputation score of 9.346. [ABSTRACT FROM AUTHOR]

Deep learning algorithms have shown exceptional effectiveness in a wide range of supervised and unsupervised learning tasks in a variety of fields, including image processing, computer vision, natural language processing, and speech or voice processing. In this paper, a comprehensive analysis is conducted to assess the impact of deep learning on user authentication using both physiological and behavioural biometrics. This work encompasses the diverse deep learning approaches employed in authentication schemes tailored for smart devices. Meticulous scrutiny of commonly used datasets in these authentication studies is undertaken, accompanied by a comparative assessment of performance. The deep learning models under consideration span a spectrum of architectures, including deep neural networks, convolutional neural networks, deep auto-encoders, recurrent neural networks, and their variants. To enhance the clarity and categorization of authentication techniques for smart devices, a taxonomy is proposed based on the specific authentication metrics employed: (1) Knowledge-based Authentication (KBA), (2) Physiological Biometric-based Authentication (PBBA), (3) Behavioural Biometric-based Authentication (BBBA), (4) Physiological and Behavioural Continuous Authentication (PBBCA), and (5) Multi Modal Authentication (MMA). Furthermore, potential contributions of deep learning techniques to the realms of physiological and behavioural biometrics are discussed. Significance is placed on performance metrics, including accuracy, stability, and robustness, in evaluating these deep learning-based authentication systems. The challenges and limitations that deep learning approaches must surmount when dealing with real-world biometric data in the context of biometric identification systems are examined. This work not only underscores the transformative role of deep learning in user authentication but also offers valuable insights into the evolving landscape of biometric identification on smart devices. An examination of performance metrics provides a holistic view of the strengths and areas for improvement in deep learning based authentication solutions. [ABSTRACT FROM AUTHOR]

Summary Background Objectives Methods Results Main limitations Conclusions Relevant ophthalmic lesions affecting vision occur in 5%–10% of all horses. Thus, knowledge of anatomy, physiology, clinical and imaging is essential for the discernment of a healthy and a pathological eye. Ultrasonography is an important diagnostic tool for ocular diseases, providing information on lesion site, extension and severity. In addition, it is possible to monitor the treatment efficiency and prognosis.This study aimed to provide biometric data about the ocular globe structures of healthy horses in order to establish a normal ultrasonographic pattern.Ocular globes (n = 40) from 20 healthy horses were evaluated by transpalpebral B‐mode ultrasonography, using an 11 MHz linear transducer.The biometrics found in this study were: globe length: 37.78 ± 0.65 mm; anterior chamber: 5.01 ± 0.27 mm; vitreous chamber: 19.19 ± 0.54 mm; lens: 12.82 ± 0.93 mm; cornea: 0.88 ± 0.11 mm.The obtained results can be more safely applied to medium‐sized horses (350 ± 43 kg).This study provides normal ocular biometrics that can be used as a reference for the diagnosis of ocular diseases, especially when the lesions are bilateral and nontranslucent cornea hinder internal structures evaluation. [ABSTRACT FROM AUTHOR]

As the number of European Union (EU) visitors grows, implementing novel border control solutions, such as mobile devices for passenger identification for land and sea border control, becomes paramount to ensure the convenience and safety of passengers and officers. However, these devices, handling sensitive personal data, become attractive targets for malicious actors seeking to misuse or steal such data. Therefore, to increase the level of security of such devices without interrupting border control activities, robust user authentication mechanisms are essential. Toward this direction, we propose a risk-based adaptive user authentication mechanism for mobile passenger identification devices for land and sea border control, aiming to enhance device security without hindering usability. In this work, we present a comprehensive assessment of novelty and outlier detection algorithms and discern OneClassSVM, Local Outlier Factor (LOF), and Bayesian_GaussianMixtureModel (B_GMM) novelty detection algorithms as the most effective ones for risk estimation in the proposed mechanism. Furthermore, in this work, we develop the proposed risk-based adaptive user authentication mechanism as an application on a Raspberry Pi 4 Model B device (i.e., playing the role of the mobile device for passenger identification), where we evaluate the detection performance of the three best performing novelty detection algorithms (i.e., OneClassSVM, LOF, and B_GMM), with B_GMM surpassing the others in performance when deployed on the Raspberry Pi 4 device. Finally, we evaluate the risk estimation overhead of the proposed mechanism when the best performing B_GMM novelty detection algorithm is used for risk estimation, indicating efficient operation with minimal additional latency. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to provide a comprehensive analysis of ocular biometric parameters in pediatric patients with cataracts to optimize surgical outcomes. By evaluating various biometric data, we seek to enhance the decision-making process for intraocular lens (IOL) placement, particularly with advanced technologies like femtosecond lasers. Methods: This retrospective comparative study included pediatric patients with cataracts who underwent ocular biometric measurements and cataract extraction with anterior vitrectomy at the Medical University of Vienna between January 2019 and December 2021. Parameters measured included corneal diameter (CD), axial length (AL), corneal thickness (CT) and flat and steep keratometry (Kf and Ks). The study explored the correlations between these parameters and IOL placement. Results: A total of 136 eyes from 68 pediatric patients were included in the study. Significant positive correlations were found between corneal diameter, age and AL. The mean CD was 11.4 mm, mean AL was 19.5 mm, CT was 581.2 ± 51.8 µm, Kf was 7.76 ± 0.55 mm and Ks 7.41 ± 0.59 mm, respectively. Older pediatric patients with larger corneal diameters and longer ALs were more likely to receive in-the-bag IOL implantation. Conversely, younger patients often required alternative IOL placements or remained aphakic. Our data indicated that over 95% of the study population and all patients aged one year and older had a corneal diameter of 10 mm or larger. Conclusions: Detailed ocular biometric analysis is crucial for optimizing both surgical outcomes and postoperative care in pediatric cataract patients. The positive correlations between CD, age and AL underline the importance of individualized surgical planning tailored to each patient's unique anatomical features. Additionally, our findings suggest that the use of a femtosecond laser is both feasible and safe for pediatric patients aged one year and older, potentially offering enhanced surgical precision and improved outcomes. [ABSTRACT FROM AUTHOR]

Human gait recognition (HGR) leverages unique gait patterns to identify individuals, but the effectiveness of this technique can be hindered due to various factors such as carrying conditions, foot shadows, clothing variations, and changes in viewing angles. Traditional silhouette-based systems often neglect the critical role of instantaneous gait motion, which is essential for distinguishing individuals with similar features. We introduce the "Enhanced Gait Feature Extraction Framework (GaitSTAR)", a novel method that incorporates dynamic feature weighting through the discriminant analysis of temporal and spatial features within a channel-wise architecture. Key innovations in GaitSTAR include dynamic stride flow representation (DSFR) to address silhouette distortion, a transformer-based feature set transformation (FST) for integrating image-level features into set-level features, and dynamic feature reweighting (DFR) for capturing long-range interactions. DFR enhances contextual understanding and improves detection accuracy by computing attention distributions across channel dimensions. Empirical evaluations show that GaitSTAR achieves impressive accuracies of 98.5%, 98.0%, and 92.7% under NM, BG, and CL conditions, respectively, with the CASIA-B dataset; 67.3% with the CASIA-C dataset; and 54.21% with the Gait3D dataset. Despite its complexity, GaitSTAR demonstrates a favorable balance between accuracy and computational efficiency, making it a powerful tool for biometric identification based on gait patterns. [ABSTRACT FROM AUTHOR]

Owing to their potential to transform traditional medical diagnostics and health monitoring, wearable biosensors have become an alternative evolutionary technology in the field of medical care. However, it is still necessary to overcome some key technique challenges, such as the selectivity, sensitivity, and stability of biometric identification. Herein, a novel, wearable electrochemical sensor based on a molecularly imprinted polymer (MIP) integrated with a copper benzene-1,3,5-tricarboxylate metal–organic framework (MOF) was designed for the detection of stress through the on-body monitoring of cortisol in sweat. The MOF was used as the substrate for MIP deposition to enhance the stability and sensitivity of the sensor. The sensor consisted of two layers, with a microfluidic layer as the top layer for spontaneous sweating and a modified electrode as the bottom layer for sensing. The sensor measured cortisol levels by detecting the current change that occurred when the target molecules bound to the imprinted cavities, using Prussian blue nanoparticles embedded in the MIP framework as the REDOX probe. The proposed sensor exhibited a linear detection range of 0.01–1000 nM with a detection limit of 0.0027 nM, and favorable specificity over other analogies. This facile anti-body free sensor showed excellent stability, and can be successfully applied for in situ cortisol monitoring. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) deployments are anticipated to reach 29.42 billion by the end of 2030 at an average growth rate of 16% over the next 6 years. These deployments represent an overall growth of 201.4% in operational IoT devices from 2020 to 2030. This growth is alarming because IoT devices have permeated all aspects of our daily lives, and most lack adequate security. IoT-connected systems and infrastructures can be secured using device identification and authentication, two effective identity-based access control mechanisms. Physical Layer Security (PLS) is an alternative or augmentation to cryptographic and other higher-layer security schemes often used for device identification and authentication. PLS does not compromise spectral and energy efficiency or reduce throughput. Specific Emitter Identification (SEI) is a PLS scheme capable of uniquely identifying senders by passively learning emitter-specific features unintentionally imparted on the signals during their formation and transmission by the sender's radio frequency (RF) front end. This work focuses on image-based SEI because it produces deep learning (DL) models that are less sensitive to external factors and better generalize to different operating conditions. More specifically, this work focuses on reducing the computational cost and memory requirements of image-based SEI with little to no reduction in performance by selecting the most informative portions of each image using entropy. These image portions or tiles reduce memory storage requirements by 92.8% and the DL training time by 81% while achieving an average percent correct classification performance of 91% and higher for SNR values of 15 dB and higher with individual emitter performance no lower than 87.7% at the same SNR. Compared with another state-of-the-art time-frequency (TF)-based SEI approach, our approach results in superior performance for all investigated signal-to-noise ratio conditions, the largest improvement being 21.7% at 9 dB and requires 43% less data. [ABSTRACT FROM AUTHOR]

Big Data (BD) is a concept that deals with enormous amounts of data storage, processing, and analysis. With the exponential advancement in the evolution of cloud computing domains in healthcare (HC), the security and confidentiality of medical records have evolved into a primary consideration for HC services and applications. There needs to be more than the present-day cryptosystems to address these troubles. Therefore, this paper introduces a novel Three-Factor Authentication (3FA) and optimal Map-Reduce (MR) framework for secure BD transmission over the cloud with Secure Hashing Authentication XOR-ed Elliptical Curve Cryptography (SHAXECC). The authentication procedure is initially carried out with the SHA-512 algorithm, which protects the network from unauthorized access. Next, data deduplication is done using the SHA-512 algorithm to eliminate duplicate files. After that, an optimal MR design is introduced to handle a large amount of BD. In an optimal MR, the mapper uses the Modified Fuzzy C-means (MFCM) clustering approach to initially form the BD clusters. Then, the reducer uses the Levy Flight and Scoring Mutation-based Chimp Optimization Algorithm (LSCOA) to form final BD clusters. Finally, the SHAXECC is used to transmit the data securely. Experiments are performed to compare the superiority of the proposed technique with the existing techniques in terms of some performance measures. The proposed approach outperformed other existing models concerning clustering and security measures. So, the proposed model is the best for data protection and privacy in cloud-enabled HC data. [ABSTRACT FROM AUTHOR]

Background: The intricate design of human anatomy reveals meaningful relationships between various body segments, which are pivotal in resolving disputes and identifying individuals. These correlations, coupled with the pronounced ethnic and geographical diversity among populations, are critical for the accurate determination of identity from unidentified remains. The practice of deducing an individual’s height based on the measurements of the thumb represents a crucial aspect of forensic anthropometry. Methods: The present study was carried out in Government Medical College Srinagar for a period of three months from January 2024 to March 2024. One hundred (50 males; 50 females) healthy individuals between 18-36 years of age and of Kashmiri origin were randomly selected for this study. Height along with thumb (right and left) dimensions was measured and correlation coefficient among these parameters was calculated. Results: Obtained were statistically significant and showed a positive correlation between stature and thumb length. Conclusions: This analysis revealing a positive correlation between an individual’s height and thumb length adds a valuable dimension to forensic anthropology and biometric identification. This correlation can be instrumental in reconstructing personal profiles from partial remains, especially in scenarios where only extremities are available for analysis. [ABSTRACT FROM AUTHOR]

This work presents a novel approach to enhancing iris recognition systems through a two-module approach focusing on low-level image preprocessing techniques and advanced feature extraction. The primary contributions of this paper include: (i) the development of a robust preprocessing module utilizing the Canny algorithm for edge detection and the circle-based Hough transform for precise iris extraction, and (ii) the implementation of Binary Statistical Image Features (BSIF) with domain-specific filters trained on iris-specific data for improved biometric identification. By combining these advanced image preprocessing techniques, the proposed method addresses key challenges in iris recognition, such as occlusions, varying pigmentation, and textural diversity. Experimental results on the Human-inspired Domain-specific Binarized Image Features (HDBIF) Dataset, consisting of 1892 iris images, confirm the significant enhancements achieved. Moreover, this paper offers a comprehensive and reproducible research framework by providing source codes and access to the testing database through the Notre Dame University dataset website, thereby facilitating further application and study. Future research will focus on exploring adaptive algorithms and integrating machine learning techniques to improve performance across diverse and unpredictable real-world scenarios. [ABSTRACT FROM AUTHOR]

The recent rapid advancements in both sensing and machine learning technologies have given rise to the universal collection and utilization of people's biometrics, such as fingerprints, voices, retina/facial scans, or gait/motion/gestures data, enabling a wide range of applications including authentication, health monitoring, or much more sophisticated analytics. While providing better user experiences and deeper business insights, the use of biometrics has raised serious privacy concerns due to their intrinsic sensitive nature and the accompanying high risk of leaking sensitive information such as identity or medical conditions. In this paper, we propose a novel modality-agnostic data transformation framework that is capable of anonymizing biometric data by suppressing its sensitive attributes while retaining features relevant to downstream machine learning-based analyses that are of research and business values. We carried out a thorough experimental evaluation using publicly available facial, voice, motion, and EEG datasets. Results show that our proposed framework can achieve a high suppression level for sensitive information, while at the same time retain underlying data utility such that subsequent analyses on the anonymized biometric data could still be carried out to yield satisfactory accuracy. [ABSTRACT FROM AUTHOR]

Endangered trout populations can be monitored with a variety of methods, the selection of which should consider social constraints and environmental variables known to affect method effectiveness. Here, we confront the effectiveness of four monitoring methods (removal with electrofishing, ELE; underwater camera survey, UCS; streamside visual survey, SVS; visual surveys with angling, VSA) to estimate the relative abundance of three populations of the endangered Mediterranean brown trout. The trout counts obtained via different methods were well correlated (r = 0.65–0.72), providing a coherent description of the relative pool abundance across the methods. However, the methods were differently affected by environmental variables, depending on the age classes of trout. Specifically, the adult and subadult counts provided by ELE and VSA were negatively and positively affected by the maximum pool depth, respectively; adult and subadult counts of VSA and the SVS were positively affected by pool area; the juvenile counts provided by the UCS were positively affected by pool shade and negatively affected by water turbidity; juvenile counts provided by VSA were positively affected by shade. Variables such as pool depth, area, shading, water turbidity and proportion of age classes can be hardly controlled in monitoring programs, and their bias could be modelled. Different sampling methods provided similar information about relative abundance and appeared equivalent. While ELE could be selected to collect samples and biometric data, monitoring relative abundance with the UCS, VSA, and SVS appears more suited and can also involve citizen scientists. [ABSTRACT FROM AUTHOR]

Introduction: Today, the approval for a generic topical product includes the presentation of therapeutic equivalence to the originator based on clinical trials. To facilitate this procedure, in 2018 the European Medicines Agency (EMA) published a draft guideline on quality and equivalence of topical products, which includes request parameters regarding the quality of the newly developed generic product and test protocols for the implementation of equivalence tests regarding efficacy. Methods: To date, no data are available on the quality and evidence of the proposed test conditions. In this study, we performed an in vitro penetration test (IVPT) following the terms of the EMA draft guideline on two authorized topical products for which therapeutic equivalence was already proven during the approval process. Results: The complex biometric data processing revealed that in vitro equivalence could not be observed for all skin sections for either originator or generic product. Moreover, the necessity of the negative control proposed in the draft guideline is more than questionable. From the results presented, there were indications that a reduced number of skin donors would be sufficient to achieve statistically significant equivalence in the comparison of all applied formulations. Here, n = 7 donors was proposed instead of n ≥ 12 as the EMA draft guideline demands, decreasing the degree of biodiversity simultaneously. Moreover, a higher number of independent replicates (n > 2) was proposed for proper statistics. Conclusion: This bioequivalence study shows insufficient parameters, which should be discussed together with the EMA draft guideline. Plain Language Summary (PLS): Today in Europe the approval of generic drugs requires clinical trials to present therapeutic equivalence to the originator. To facilitate this time- and cost-intensive process, the European Medicines Agency (EMA) published a draft guideline in 2018 proposing test protocols for carrying out equivalence tests for drugs applied on human skin. Although its publication is over 5 years old, it is still in a draft version; moreover, so far no evidence has been presented on whether the experimental settings in the test protocols can prove equivalence. This study should prove the EMA test protocol for in vitro permeation tests (IVPT) as closely as possible on two authorized topical products for which therapeutic equivalence was already proven during the approval process to determine whether their equivalence could be observed by this pharmacokinetic approach as well. Due to the experimental setup, a penetration test was performed to survey the drug diffusion behavior over the skin cross section instead of the amount of drug which permeates through the skin. To the best of our knowledge, this study is the first work published regarding that topic. Here, bioequivalence of the preparations applied, which were equivalent when they were tested on patients during their approval, was not found overall. We recommend reducing the number of donors and increasing the number of replicates to achieve a higher power for the statistical data evaluation. Moreover, an evaluation of the drug amount at the pharmaceutical place of action should be considered in the EMA guideline. [ABSTRACT FROM AUTHOR]

This paper aims to propose innovative actions of advanced technological solutions and consequent necessary institutional decisions to achieve in a reasonable time the definitive confidential data protection and data sovereignty, based on available scientific results. Confidential data protection is a fundamental and strategic issue in next-generation Internet systems to guarantee data sovereignty and the respect of human rights as stated in the foundation of the United Nations. Even if presently many international regulations are decisive steps to guarantee data protection within normative contexts, they are not adequate to face new technologies, such as facial recognition, automatic profiling, position tracking, biometric data, AI applications, and many others in the future, as they are implemented without any awareness by the interested subjects. Therefore, a new approach to data protection is mandatory based on innovative and disruptive technological solutions. A recent OECD report highlighted the need for the so-called Privacy-Enhancing Technologies (PETs) for the effective protection of confidential data, even more urgent for the coexistence of privacy and data sharing in international contexts. A common feature of these technologies is the use of software methodologies that can run on currently available microprocessors and their present immaturity. More effective and definitive protection can be achieved with another methodological approach based on the paradigm of 'Data Usage Control'. This new concept guarantees data protection policy by default and initial design and it requires a new architecture of the data and a new HW&SW architecture of the computers. This contribution has a two-fold objective: first, to clarify why regulations alone and present technological proposals are not adequate for the effective and definitive protection of data and, second, to indicate the new necessary technological approach and the simultaneous institutional actions required to achieve the definitive protection and sovereignty of data in reasonable times, based on the results already available in the scientific literature. [ABSTRACT FROM AUTHOR]

The advancement of technology and its developments have provided the forensic sciences with many cutting-edge tools, devices, and applications, allowing forensics a better and more accurate understanding of the crime scene, a better and optimal acquisition of data and information, and faster processing, allowing more reliable conclusions to be obtained and substantially improving the scientific investigation of crime. This article describes the technological advances, their impacts, and the challenges faced by forensic specialists in using and implementing these technologies as tools to strengthen their field and laboratory investigations. The systematic review of the scientific literature used the PRISMA® methodology, analyzing documents from databases such as SCOPUS, Web of Science, Taylor & Francis, PubMed, and ProQuest. Studies were selected using a Cohen Kappa coefficient of 0.463. In total, 63 reference articles were selected. The impact of technology on investigations by forensic science experts presents great benefits, such as a greater possibility of digitizing the crime scene, allowing remote analysis through extended reality technologies, improvements in the accuracy and identification of biometric characteristics, portable equipment for on-site analysis, and Internet of things devices that use artificial intelligence and machine learning techniques. These alternatives improve forensic investigations without diminishing the investigator's prominence and responsibility in the resolution of cases. [ABSTRACT FROM AUTHOR]

The increasing frequency and duration of marine heatwaves (MHWs) due to climate change pose severe threats to aquaculture, causing drastic physiological and growth impairments in farmed fish, undermining their resilience against additional environmental pressures. To ensure sustainable production that meets the global seafood demand and animal welfare standards, cost-effective and eco-friendly strategies are urgently needed. This study explored the efficacy of the red macroalga Asparagopsis taxiformis on juvenile white seabream Diplodus sargus reared under optimal conditions and upon exposure to a MHW. Fish were fed with four experimental diets (0%, 1.5%, 3% or 6% of dried powdered A. taxiformis) for a prophylactic period of 30 days (T30) and subsequently exposed to a Mediterranean category II MHW for 15 days (T53). Biometric data and samples were collected at T30, T53 and T61 (8 days post-MHW recovery), to assess performance indicators, biomarker responses and histopathological alterations. Results showed that A. taxiformis supplementation improved catalase and glutathione S-transferase activities and reduced lipid peroxidation promoted by the MHW, particularly in fish biofortified with 1.5% inclusion level. No histopathological alterations were observed after 30 days. Additionally, fish biofortified with 1.5% A. taxiformis exhibited increased citrate synthase activity and fish supplemented with 1.5% and 3% showed improved digestive enzyme activities (e.g., pepsin and trypsin activities). Overall, the present findings pointed to 1.5% inclusion as the optimal dosage for aquafeeds biofortification with A. taxiformis, and confirmed that this seaweed species is a promising cost-effective ingredient with functional properties and great potential for usage in a climate-smart context. [ABSTRACT FROM AUTHOR]

We introduce a novel privacy-preserving biometric authentication scheme based on irises that allows a user to enroll once at a trusted biometric certification authority (BCA) and authenticate to online service providers (SPs) multiple times without involving the BCA during the authentication. Our scheme preserves the user's biometric privacy from the SPs and transactional privacy from the BCA, while providing security against a malicious user. During the enrollment, the BCA issues a signed token that encrypts the user's biometrics. We introduce techniques enabling the SP and the user to perform secure computation of biometric matching between such encrypted biometrics and the user's biometrics captured at the authentication time. We provide a prototype implementation, a performance evaluation, and a security analysis of the protocol. [ABSTRACT FROM AUTHOR]

(1) Background: This study compares the effective lens position (ELP) and intraocular lens power (IOLP) derived from SRK/T, Hoffer Q, Holladay I, and Haigis formulas with the actual lens position (ALP) and the implanted IOLP after cataract surgery. Additionally, it aims to optimize ALP using a ray-tracing-based simulated optical model to achieve emmetropia. (2) Methods: A retrospective observational study was conducted on 43 eyes implanted with the same monofocal intraocular lens (IOL). Preoperative and postoperative biometric data were collected using the Lenstar LS900. Postoperative measurements included ALP, subjective refraction, and refraction error (RE). Optical simulations (OSLO EDU 6.6.0) were utilized to optimize ALP for emmetropia (ALPIDEAL). (3) Results: Paired t-test results between REOSLO-REOBJ (p-value = 0.660) and REOSLO-RESUB (p-value = 0.789) indicated no significant statistical differences. However, statistically significant differences were found between ALP and ALPIDEAL (p < 0.05), with a difference of −0.04 ± 0.45 mm [ranging from −1.00 to 1.20 mm]. A significant correlation was observed between ΔALP (ΔALP = ALP − ALPIDEAL) and RESUBJ. (4) Conclusions: This customized ray-tracing eye model effectively achieves refractive outcomes similar to those obtained both subjectively and objectively post-surgery. Additionally, it has enabled optical simulations to optimize the IOL position and achieve emmetropia. [ABSTRACT FROM AUTHOR]

Visible and near-infrared (Vis–NIR) spectroscopy technology can better meet the needs of identification of mineral differences in iron ores by using a large amount of chemical bond vibration information contained in the spectra. In this paper, the original Vis–NIR spectra data of six kinds of iron ore are dimensionally reduced by the principal component analysis (PCA) algorithm, and the influence of sample noise and outliers is eliminated at the same time. After processing, the sum of the contribution rates of the first three principal components of the reflectance spectra reaches 99.27 pct, and the eigenvalue inclusion rate of the principal component analysis data is high, which has a good data processing effect. Then, based on the random forest (RF) algorithm, the spectra data after PCA are classified and predicted. After multiple authentications, the identification accuracy of the algorithm can reach more than 95 pct on average. Finally, the receiver operating characteristic (ROC) curve is used to characterize the prediction success rate of the recognition model and its accuracy for the recognition of single iron ore. [ABSTRACT FROM AUTHOR]

Gesture recognition integrated into Internet of Things (IoT) systems is an emerging area of research with significant potential for various applications. This system primarily focuses on recognizing hand gestures to facilitate communication, especially for individuals with speech impairments. The proposed hand gesture recognition system employs sensors, such as flex sensors and accelerometers, to accurately capture hand movements. These sensors are connected to an IoT device, which processes the data and translates gestures into meaningful commands or text. The system is designed to be cost-effective, making it accessible for users who require assistive technologies. One notable application of this technology is in aiding communication for speech-impaired individuals. By converting recognized gestures into text or speech, the system enables users to interact more effectively with their environment and with others. The integration of machine learning algorithms enhances the accuracy and efficiency of gesture recognition, allowing for real-time processing and feedback. Overall, the hand gesture recognition system using IoT aims to improve accessibility and communication for differently-abled individuals, showcasing the potential of combining hardware and software solutions to address real-world challenges in human-computer interaction. [ABSTRACT FROM AUTHOR]

Social media blockchain is emerging as a promising solution to deal with privacy issues, by putting user privacy in edge nodes rather than centralized nodes. Under the protection of information encryption, only those who have cryptographic keys can get access to key information. This work aims at multimedia information in social media blockchain and utilizes the RSA encryption mechanism to construct the information encryption system. Due to the resilience of biometric features, the biometric cryptographic keys are not easy to be fabricated. Thus, this paper proposes a biometric keys-enhanced multimedia encryption algorithm for social media blockchain. First of all, the wiener filter is adopted to make some preprocessing operations to images, such as noise reduction. On this basis, the discrete wavelet transform is adopted to extract feature representation from images, and nonlinear approximation of contourlet transform is adopted to make feature fusion. Next, cryptographic keys can be generated from the fused biometric feature vectors to encrypt biometric data. Finally, some simulation experiments are conducted to evaluate performance of the proposal from three aspects: key generation time, security level and encryption-decryption time complexity. For key generation time, processing speed of the proposal is approximately 1–2 ms per sample. For security level, the proposal can reach an index value beyond 95% which is higher than comparison methods. For encryption-decryption time complexity, the proposal is about 30% lower than comparison methods. [ABSTRACT FROM AUTHOR]

Advancements in cloud computing, flying ad-hoc networks, wireless sensor networks, artificial intelligence, big data, 5th generation mobile network and internet of things have led to the development of smart cities. Owing to their massive interconnectedness, high volumes of data are collected and exchanged over the public internet. Therefore, the exchanged messages are susceptible to numerous security and privacy threats across these open public channels. Although many security techniques have been designed to address this issue, most of them are still vulnerable to attacks while some deploy computationally extensive cryptographic operations such as bilinear pairings and blockchain. In this paper, we leverage on biometrics, error correction codes and fuzzy commitment schemes to develop a secure and energy efficient authentication scheme for the smart cities. This is informed by the fact that biometric data is cumbersome to reproduce and hence attacks such as side-channeling are thwarted. We formally analyze the security of our protocol using the Burrows–Abadi–Needham logic logic, which shows that our scheme achieves strong mutual authentication among the communicating entities. The semantic analysis of our protocol shows that it mitigates attacks such as de-synchronization, eavesdropping, session hijacking, forgery and side-channeling. In addition, its formal security analysis demonstrates that it is secure under the Canetti and Krawczyk attack model. In terms of performance, our scheme is shown to reduce the computation overheads by 20.7% and hence is the most efficient among the state-of-the-art protocols. [ABSTRACT FROM AUTHOR]

Smartphone-based biometric authentication has been widely used in various applications. Among several biometric characteristics, fingerphoto biometrics captured from smartphones are gaining popularity owing to their usability, scalability across different smartphones, and reliable verification. However, fingerphoto verification systems are vulnerable to both direct and indirect attacks. In this work, we propose a novel method to generate morphing attacks on fingerphoto biometrics captured using smartphones. We introduce three different image-level fingerphoto morphing attack generation algorithms that can generate high-quality fingerphoto morphing images with minimum distortions. Extensive experiments were conducted on two datasets captured using different smartphones under various environmental conditions. The results demonstrate that the proposed morphing algorithms are highly vulnerable to commercial off-the-shelf and block-directional fingerprint verification systems. To effectively detect morphing attacks on fingerphoto biometrics, we propose the use of fingerphoto morphing attack detection algorithms that utilize both handcrafted and deep features. However, our detection results showed a high error rate in accurately detecting these types of attacks. [ABSTRACT FROM AUTHOR]

Biometric security methods deploy any type of biometric recognition for safety purposes. Different from unimodal biometric system that uses single trait for security, the multimodal biometrical authentication (MBA) system deploys two or additional diverse biometric traits for authentication. In addition, MBA systems incurs high cost and have high level of security, as 2 biometric traits are complex for intruders to counterfeit more than a single trait. A new Ensemble Recognition Model with Optimal Training for Multimodal Biometric Authentication (ERMOTMBA) model is proposed in this work using Self Upgraded NBO (SU-NBO) algorithm. At first, median filtering is deployed to pre-process the palm print image, finger vein image, finger knuckle image, iris image and finger print image. The features extracted for palm print, finger vein, finger knuckle, iris and finger print are as follows: Palm print – extracting statistical features and line features, Finger vein – extracting bifurcation point and improved LGBP, Finger knuckle- Global feature and local feature, Iris- extracting 2D Gabor kernel and polynomial filtering, Finger print- Improved Minutiae feature (Binarization and thinning). After the extraction of feature set, enhanced feature level fusion (FLF) takes place under correlation basis. Finally, EC (with the combination of DBN, CNN and Bi-LSTM) is exploited, whose outputs are averaged to finalize the recognition outcome. Especially, proposing a training model for tuning the EC weights, and that is termed as SU-NBO algorithm. [ABSTRACT FROM AUTHOR]

Due to the rapid growth in the field of information transmission techniques, billions of data are transmitted through variant authentication applications between clients. Providing protection to store and transmit these details has the priority in most services. Cancelable biometric techniques have the ability to store and employ these biometric signatures safe away any intruders. These template protection techniques are derived into transformation and cipher based cancelable biometric methodologies. The cipher approaches that are dependent on chaotic schemes quite achieve the two requirements of successful cancelable systems known by confusion and diffusion. This is prompted us to propose a novel cancelable research combines between Hyperchaotic technique and Fibonacci Q-matrix. The hyperchaotic system employs six initial conditions, equations, to construct the initial deformed patterns. Afterwards, the stage of diffusion is accomplished by employing Fibonacci Q-matrix for the resultant dislocated patterns to obtain the final protected template. The proposed cancelable authentication technique is validated by the two evaluation perspectives of authentication and security measurements, respectively. Furthermore, the proposed cancelable system is compared with the most recent studies in the same state-of-art which are utilized by GA and RNA-GA. Five tested biometrics are exploited to prove the validation of our work through any gray or colored biometric patterns with different sizes under different capturing environments even in the presence of noise level. The proposed work achieves high authentication performance by large values of AROC (Area under Receiver Operating Characteristic) Curve leads to 0.999, and similarity coefficients of licensed clients. As well as, the least values of FAR, and FRR (False Acceptance, and False Rejection Ratios) in addition the more uniform resultant histogram shapes due to the cancelable patterns. From the security perspective, the proposed cancelable methodology achieves high entropy of 7.99, NPCR (Number of Pixels Change Rate), and UACI (Unified Average Changing Intensity) values lead to almost optimum results of 99.0271%, and 31.2058%, respectively. This validates its immunity against probable differential attacks. [ABSTRACT FROM AUTHOR]

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

In Moroccan higher education, the integrity of examinations is paramount, yet it faces the persistent challenge of identity impersonation. This form of academic dishonesty not only undermines the credibility of educational institutions but also contributes to the graduation of incompetent students. In an era where artificial intelligence is revolutionizing various sectors, its application to upholding academic integrity is both timely and essential. This paper proposes an advanced deep learning-driven fingerprint verification model specifically designed to combat impersonation in university examinations. Unlike traditional methods, our model leverages the power of Siamese neural networks (SNN), renowned for their effectiveness in learning distinct features and similarities. The model, trained, validated, and tested using the SOCOFing dataset, demonstrated high accuracy and effectiveness in fingerprint identification, crucial for verifying identities in educational exam settings. It achieved an accuracy of 99.29%, and an F1 score of 99.27%, surpassing other systems and significantly contributing to examination integrity in Moroccan higher education. [ABSTRACT FROM AUTHOR]

Including sex in IOL formulas may reveal new data patterns using artificial intelligence. In addition, the presented IOL distribution is a significant advancement to reduce the labeling step to 0.25 D. Purpose: To describe gender differences in the biometric parameters of a large sample of patients with cataract. Cataract surgery has evolved from a vision restoration to a refractive procedure, and population-based studies are vital to optimize normative databases and postsurgical outcomes. Setting: Miguel Servet University Hospital, Zaragoza, Spain. Design: Retrospective single-center observational study. Methods: The study included 34 589 eyes (20 004 patients with cataract). Biometric data were obtained from IOL Master 700 and Pentacam HR. Linear mixed models were used to account for intereye correlation. HofferQST formula was used to calculate the hypothetical distribution of intraocular lens (IOL) power (arbitrary lens; A = 119.2). Results: Most biometric variables showed significant differences between sexes (P <.0001), such as 0.53 mm shorter eyes found in females, of which 0.16 mm are explained by shorter aqueous depth. Steeper anterior keratometries (∼0.75 diopter [D]) were found in women, to end up in no difference on anterior astigmatism magnitude, but different orientation (P <.0001). The distribution of IOL power differed between sexes (P <.001), with the interquartile range shifting 1 D toward more powerful lenses in women and odds ratio (power >26 D) = 2.26, P <.0001 (Fisher). Conclusions: Large sample size studies provide smaller margin of error, higher power, and controlled risk of reporting false (negative or positive) findings. Highly significant differences between sexes in ocular biometry were found; this supports the idea that including sex as a parameter in IOL calculation should be explored and may improve results. In addition, the distribution of IOL powers was provided, which may be useful for manufacturers and hospital stock planning. [ABSTRACT FROM AUTHOR]

As a cornerstone of democratic governance, elections hold unparalleled significance, shaping a nation's trajectory. However, the prevailing ballot-paper based voting systems continue to face trust issues among significant populations. As a result, e-Voting has emerged as an appealing alternative, with numerous countries opting for its implementation globally. While e-Voting systems offer several advantages, they also come with their own set of challenges. Even a minor vulnerability can lead to massive manipulations in voting results. In recent years, there have been efforts to revolutionize the e-Voting paradigm by harnessing the potential of emerging technologies such as biometrics and blockchain. This paper proposes a Internet-based voting that adopts blockchain technology and biometric identification techniques. We use biometric modalities, such as fingerprint and facial recognition, for voter authentication while leveraging Hyperledger Fabric framework as blockchain network and ensuring a secure, transparent, and tamper-evident voting record. We demonstrate the proposed system with 100 participants in a preset environment where we collect the biometrics data. The results indicate that 87% of participants successfully registered with biometrics, while 88% cast their votes with a combination of either voter ID and fingerprint or voter ID with facial recognition. Our findings suggest that the proposed system allows voters to access the system seamlessly and automate identity verification procedures while ensuring a secure, decentralized, and distributed database network that maintains transparency. Future research shall be carried out in collaboration with election officials and voters to improve the system in real-world scenarios. [ABSTRACT FROM AUTHOR]

The significant increase in online activities in the wake of recent global events has underlined the importance of biometric person authentication on digital platforms. Although many biometric devices may be used for precise biometric authentication, acquiring the necessary technology, such as 3D sensors or fingerprint scanners, can be prohibitively expensive and logistically challenging. Addressing the demands of online environments, where access to specialized hardware is limited, this paper introduces an innovative approach. In this work, by fusing static and dynamic signature data with facial data captured through regular computer cameras, a dataset of 1750 samples from 25 individuals is constructed. Deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and temporal convolutional networks (TCN), are employed to craft a robust multi-classification model. This integration of various deep learning algorithms has demonstrated remarkable performance enhancements in biometric authentication. This research also underscores the potential of merging dynamic and static biometric features, derived from readily available sources, to yield a high-performance recognition framework. As online interactions continue to expand, the combination of various biometric modalities holds potential for enhancing the security and usability of virtual environments. [ABSTRACT FROM AUTHOR]

Over the last decade, Online Social Media platforms have witnessed a dramatic expansion due to the substantial reliance of individuals on these communication channels. These platforms are widely utilized to convey emotions, share opinions, and express preferences through various means such as artworks, multimedia contents, and blogs. Researchers are exploring these individual-specific traits for biometric identification. Aesthetic biometric systems utilize users' unique preferences across various subjective forms such as images, music, and textual contents. This study introduces a novel multi-modal aesthetic system, with a primary contribution to the development of an attention-based fusion method for person identification. The proposed identification system leverages a deep pre-trained model for high-level feature extraction from visual and auditory modalities. The paper introduces a novel fusion architecture named attention-based residual fusion network (ARF-Net) to incorporate two heterogeneous aesthetic feature vectors. The proposed model yielded a 99.38% identification accuracy on the Aesthetic Image Audio 32 (AIA32) dataset and 98.02% identification accuracy on Aesthetic Image Audio 52 (AIA52) dataset, outperforming other aesthetic biometric systems. The proposed architecture stands out for its efficiency, showcasing a lightweight architecture with minimal parameters, ensuring optimal performance in different modalities. [ABSTRACT FROM AUTHOR]

The authors explore ways for art educators to address the functioning of facial detection algorithms and their potential issues through critical race theory (CRT). Topics discussed include the censoring of CRT across the U.S., the significance of biometric data privacy, and examples of algorithms that perpetuate racism.

Secure protection of sensitive data and financial transactions is of the utmost importance in the dynamic world of online trade. In this study, we present a full-stack security architecture that uses five separate algorithms: ECF, Transaction Anomaly Detection, Adaptive Threat Intelligence, Behavioral Biometric Authentication, and Dynamic Encryption Protocol. By creating encryption keys on the fly while the user logs in, the DEP method lays a solid groundwork for safe data transfer. Behavioral biometric authentication (BBA) uses DEP output to verify users based on their distinct behavior, which is an extra layer of security. By combining both current and past threat information, the ATI algorithm is able to constantly adjust security protocols, providing a preventative shield against new dangers. TAD is an expert at detecting anomalies in online purchases, which helps keep financial transactions honest. When ECF and DEP work together, they filter email content, making communication more secure. Flowcharts help to illustrate the interactions between various algorithms, which helps to understand their operations in detail. Every algorithm's importance is brought to light by an ablation study, which shows how each one contributes and how they all work together to affect the overall security posture. The suggested security framework outperforms the state-of-the-art in terms of efficacy, adaptability, and usability, according to performance evaluations conducted using a number of metrics. These findings can help decision-makers build a strong security plan that is specific to the challenges of online shopping. To conclude, the suggested framework is an integrated and complementary strategy that will strengthen online trade in the face of several cyber dangers while simultaneously protecting the confidentiality, authenticity, and availability of all associated communications and transactions. [ABSTRACT FROM AUTHOR]

All Educational Institutions prioritize student attendance and Baze University is certainly one to uphold the standard. All students are required to maintain a minimum attendance rate of at least 75% in each class or lecture to be eligible to take the module's examination. With the advancement of technology, biometrics concepts have proven to be more efficient in monitoring attendance. We present a facial recognition-based prototype webapp for Baze university. This webapp aims to eradicate the cumbersome method of marking attendance that lecturers of Baze University have subscribed to. The main focus is the use of facial recognition techniques using the Dlib library of OpenCV. Images are read and stored in a database, compared with images captured from a webcam to analyze and record the best matches using nearest neighbor algorithm. [ABSTRACT FROM AUTHOR]

Handwritten signatures in biometric authentication leverage unique individual characteristics for identification, offering high specificity through dynamic and static properties. However, this modality faces significant challenges from sophisticated forgery attempts, underscoring the need for enhanced security measures in common applications. To address forgery in signature-based biometric systems, integrating a forgery-resistant modality, namely, noninvasive electroencephalography (EEG), which captures unique brain activity patterns, can significantly enhance system robustness by leveraging multimodality's strengths. By combining EEG, a physiological modality, with handwritten signatures, a behavioral modality, our approach capitalizes on the strengths of both, significantly fortifying the robustness of biometric systems through this multimodal integration. In addition, EEG's resistance to replication offers a high-security level, making it a robust addition to user identification and verification. This study presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms based on mental, & motor imagery, and physical execution: i) thinking of the signature's image, (ii) drawing the signature mentally, and (iii) drawing a signature physically. Extensive experiments have been conducted to establish a baseline with machine learning classifiers. The results demonstrate that multimodality in biometric systems significantly enhances robustness, achieving high reliability even with limited sample sizes. We release the raw, pre-processed data and easy-to-follow implementation details. [ABSTRACT FROM AUTHOR]

The blockchain has continued to showcase myriads of promises as a breakthrough tech and highly adapted in manifold sectors. Its adoption is accompanied by a range of issues that make implementation complicated. To aid successful implementation, a variety of frameworks have been developed. But, selecting the appropriate framework based on the conformity of its features with the financial sector is a challenge for decision-makers. This study seeks to provide solution to the challenge of synchronizing entire blockchain in a minimum possible time, and create a Blockchain Technology Framework for pricing and Evaluating Financial Markets and also simulate the cryptocurrency market data using a neural network based application to generate the optimal prediction and predicting the future of investment by fusing the facial recognition system with the framework. [ABSTRACT FROM AUTHOR]

With an upsurge of data by global brands to interact/reach prospective clients, the birth of the Internet has today bridged the information gap. Virtualization techniques are today utilized as means to bridge the various lapses in our human processing endeavors. The adoption of tech to perform a variety of functions has since become imperative to ease our daily living as well as seamlessly allow transformations of various kinds to be impacted on our society. Study proposes a virtual key card access with cost-effective and cheap solution for managing access to areas within a facility. We have successfully integrated IoTs, virtual key card access, web-access control, solenoid lock integration, and ESP32-controller to create a comprehensive access control system. Its benefits over traditional key includes better security, user data privacy, system efficiency, and user convenience. The system also provides real-time monitor and control capabilities that will allow administrators to track and manage access to the facility remotely. And in turn, enhancing system's security and efficiency. [ABSTRACT FROM AUTHOR]

Today, more than ever before, technological progress is evolving rapidly, and in the absence of adequate regulatory frameworks, the big players in the digital market (the so-called Big Techs) are exploiting personal data (name, address, telephone numbers) and private data (political opinions, religious beliefs, financial information, or health status) in an uncontrolled manner. A crucial role in this scenario is played by the weakness of international regulatory frameworks due to the slow response time of legislators who are incapable, from a regulatory point of view, of keeping pace with technological evolution and responding to the new requirements coming from the social context, which is increasingly characterized by the pervasive presence of new technologies, such as smartphones and wearable devices. At the European level, the General Data Protection Regulation (GDPR) and the Regulation on Electronic Identification, Authentication and Trust Services (eIDAS) have marked a significant turning point in the regulatory landscape. However, the mechanisms proposed present clear security issues, particularly in light of emerging concepts such as digital identity. Moreover, despite the centrality of biometric issues within the European regulatory framework and the practical introduction of biometric data within electronic national identity (eID) cards, there are still no efforts to use biometric features for the identification and authentication of a person in a digital context. This paper clarifies and precisely defines the potential impact of biometric-based digital identity and hypothesizes its practical use for accessing network-based services and applications commonly used in daily life. Using the Italian eID card as a model, an authentication scheme leveraging biometric data is proposed, ensuring full compliance with GDPR and eIDAS regulations. The findings suggest that such a scheme can significantly improve the security and reliability of electronic identification systems, promoting broader adoption of eIDAS solutions. [ABSTRACT FROM AUTHOR]

The paper is dedicated to issues of the conception of migration security in legislation and doctrine of the Administrative Law. It is stressed in the paper that migration processes in the modern world are a scope of the variety of social relations of the comprehensive safety, legal, management, and economic character that are related to the replacement of residents and non-residents of Ukraine into the state and out of it. This replacement is an object of administrative impact and administration and has an appropriate goal and task. It is suggested to define migration security as a state of security of whole the scope of strategic and regional migration interests of Ukraine, an absence or minimization of internal and external factors of risks. There is outlined, a purpose of governmental impact in the field of migration processes is insuring organization and managemental, and legislative impacts on the migration as a social phenomenon and its specifical expressions; formation of the content of migration relationships, determining the status of its participants; and impacting on factors which lead to violation of established requirements, illegal migration, and which are an object of attention of the state policy within the counteraction to this phenomenon. It is emphasized, that means of ensuring migration security should be current institutional elements of the migration system, built based on wide usage of information technologies, digitalization of management processes and making administrative-managemental solutions, integration of databases, and wide use of biometric identification tools, professional training of officials of the State Migration Service, and clear regulative regulation of activities. Compliance with the requirements of international cooperation and the state's unwavering fulfillment of international legal obligations, organization and legal ensuring transparent visa policy and support, balanced institutional system of the administration of migration processes, active law enforcement activities and cooperation, the use of the latest means of migration control through the use of biometric document flow, maintaining demographic and other types of registers, with unconditional compliance with the requirements for the protection of personal data, ensuring the administrative and legal possibility of interaction between the subjects of migration policy are significant principles of the state migration policy. [ABSTRACT FROM AUTHOR]

The investigation describes an inventive use of digital twin technology and LSTM-based machine learning models for real-time patient lung disease monitoring and nutrition planning. The suggested application uses various patient healthcare data, treatment processes, dietary habits, and real-time sensor information to construct digital twins, which are virtual reproductions of specific patients. The LSTM model is trained on this large dataset to predict patient health improvements and dietary needs. For each patient's digital twin, the program provides personalized treatment plans and nutritional advice, enabling proactive interventions and optimizing patient care. Using performance measures, the trained LSTM model achieves high scores for accuracy (92%), precision (89%), recall (93%), and F1 score (91%), proving its usefulness in generating credible health predictions. Patient feedback on the program shows that patients (98.8%) agree on the accuracy and importance of health feedback, as well as the convenience of access to health information (95.4%). The application's response rate study reveals an average response rate of 85.87%, assuring prompt feedback. To secure patient information, the study emphasizes data privacy and security, adopting multilayered authentication and data encryption. The outcomes of this study demonstrate the application's potential to revolutionize patient-centered healthcare by providing data-driven, personalized solutions to patients and healthcare professionals. [ABSTRACT FROM AUTHOR]