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How to analyze data when there is violation of the positivity assumption? Several possible solutions exist in the literature. In this paper, we consider propensity score (PS) methods that are commonly used in observational studies to assess causal treatment effects in the context where the positivity assumption is violated. We focus on and examine four specific alternative solutions to the inverse probability weighting (IPW) trimming and truncation: matching weight (MW), Shannon's entropy weight (EW), overlap weight (OW), and beta weight (BW) estimators. We first specify their target population, the population of patients for whom clinical equipoise, that is, where we have sufficient PS overlap. Then, we establish the nexus among the different corresponding weights (and estimators); this allows us to highlight the shared properties and theoretical implications of these estimators. Finally, we introduce their augmented estimators that take advantage of estimating both the propensity score and outcome regression models to enhance the treatment effect estimators in terms of bias and efficiency. We also elucidate the role of the OW estimator as the flagship of all these methods that target the overlap population. Our analytic results demonstrate that OW, MW, and EW are preferable to IPW and some cases of BW when there is a moderate or extreme (stochastic or structural) violation of the positivity assumption. We then evaluate, compare, and confirm the finite-sample performance of the aforementioned estimators via Monte Carlo simulations. Finally, we illustrate these methods using two real-world data examples marked by violations of the positivity assumption., (© 2024 Wiley‐VCH GmbH.)

Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar-valued or multivariate data, but FDA brings additional challenges due to the high- and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field. The paper covers fundamental concepts such as descriptives and outliers, smoothing, amplitude and phase variation, and functional principal component analysis. It also discusses functional regression, statistical inference with functional data, functional classification and clustering, and machine learning approaches for functional data analysis. The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data. Sparse functional data methods are also relevant for longitudinal data analysis. All presented methods are demonstrated using available software in R by analyzing a dataset on human motion and motor control. To facilitate the understanding of the methods, their implementation, and hands-on application, the code for these practical examples is made available through a code and data supplement and on GitHub., (© 2024 The Author(s). Biometrical Journal published by Wiley‐VCH GmbH.)

Finlay-Wilkinson regression is a popular method for modeling genotype-environment interaction in plant breeding and crop variety testing. When environment is a random factor, this model may be cast as a factor-analytic variance-covariance structure, implying a regression on random latent environmental variables. This paper reviews such models with a focus on their use in the analysis of multi-environment trials for the purpose of making predictions in a target population of environments. We investigate the implication of random versus fixed effects assumptions, starting from basic analysis-of-variance models, then moving on to factor-analytic models and considering the transition to models involving observable environmental covariates, which promise to provide more accurate and targeted predictions than models with latent environmental variables., (© 2024 The Author(s). Biometrical Journal published by Wiley‐VCH GmbH.)

Purpose: To use a combination of partial least squares regression and a machine learning approach to predict intraocular lens (IOL) tilt using preoperative biometry data., Setting: Kepler University Clinic Linz, Linz, Austria., Design: Prospective single-center study., Methods: Optical coherence tomography, autorefraction, and subjective refraction were performed at baseline and 8 weeks after cataract surgery. In analysis I, only 1 eye per patient was included and a tilt prediction model was generated. In analysis II, a pairwise comparison between right and left eyes was performed., Results: In analysis I, 50 eyes of 50 patients were analyzed. Difference in amount, orientation, and vector from preoperative to postoperative lens tilt was -0.13 degrees, 2.14 degrees, and 1.20 degrees, respectively. A high predictive power (variable importance for projection [VIP]) for postoperative tilt prediction was found for preoperative tilt (VIP = 2.2), pupil decentration (VIP = 1.5), lens thickness (VIP = 1.1), axial eye length (VIP = 0.9), and preoperative lens decentration (VIP = 0.8). These variables were applied to a machine learning algorithm resulting in an out of bag score of 0.92 degrees. In analysis II, 76 eyes of 38 patients were included. The difference of preoperative to postoperative IOL tilt of right and left eyes of the same individual was statistically relevant., Conclusions: Postoperative IOL tilt showed excellent predictability using preoperative biometry data and a combination of partial least squares regression and a machine learning algorithm. Preoperative lens tilt, pupil decentration, lens thickness, axial eye length, and preoperative lens decentration were found to be the most relevant parameters for this prediction model., (Copyright © 2024 Published by Wolters Kluwer on behalf of ASCRS and ESCRS.)

This paper addresses issues concerning biometric authentication based on handwritten signatures. Our research aimed to check whether a handwritten signature acquired with a mobile device can effectively verify a user's identity. We present a novel online signature verification method using coordinates of points and pressure values at each point collected with a mobile device. Convolutional neural networks are used for signature verification. In this paper, three neural network models are investigated, i.e., two self-made light SigNet and SigNetExt models and the VGG-16 model commonly used in image processing. The convolutional neural networks aim to determine whether the acquired signature sample matches the class declared by the signer. Thus, the scenario of closed set verification is performed. The effectiveness of our method was tested on signatures acquired with mobile phones. We used the subset of the multimodal database, MobiBits, that was captured using a custom-made application and consists of samples acquired from 53 people of diverse ages. The experimental results on accurate data demonstrate that developed architectures of deep neural networks can be successfully used for online handwritten signature verification. We achieved an equal error rate (EER) of 0.63% for random forgeries and 6.66% for skilled forgeries. [ABSTRACT FROM AUTHOR]

A signature is a biometric attribute commonly used for identity verification. It can be represented by a shape created with a classic pen, but it can also contain dynamic information. This information is acquired using a digital input device, such as a graphic tablet or a digital screen and stylus. Information about the dynamics of the signing process is stored in the form of signals that change over time, including pen velocity, pressure, and more. These dynamics are characteristic of an individual and are difficult for a human to forge. However, it is an interesting research issue whether the values of signals describing a dynamic signature can be predicted using artificial intelligence methods. Predicting the dynamics of the signals describing a signature would benefit various scientific problems, including improving the quality of reference signals by detecting anomalies, creating signature templates better suited to individuals, and more effectively detecting potential forgeries by identity verification systems. In this paper, we propose a method for predicting dynamic signature signals using an artificial neural network. The method was evaluated using samples collected in the DeepSignDB database, distributed by BiDA Lab. [ABSTRACT FROM AUTHOR]

Biostatistics plays a pivotal role in developing, interpreting and drawing conclusions from clinical, biological and epidemiological data. However, the improper application of statistical methods can lead to erroneous conclusions and misinterpretations. This paper provides a comprehensive examination of the most frequent mistakes encountered in the biostatistical analysis process. We identified and elucidated 10 common errors in biostatistical analysis. These include using the wrong metric to describe data, misinterpreting P -values, misinterpreting the 95% confidence interval, misinterpreting the hazard ratio as an index of prognostic accuracy, ignoring the sample size calculation, misinterpreting analysis by strata in randomized clinical trials, confusing correlation and causation, misunderstanding confounders and mediators, inadequately codifying variables during the data collection, and bias arising when group membership is attributed on the basis of future exposure in retrospective studies. We discuss the implications of these errors and propose some practical strategies to mitigate their impact. By raising awareness of these pitfalls, this paper aims to enhance the rigor and reproducibility of biostatistical analyses, thereby fostering more robust and reliable biomedical research findings. [ABSTRACT FROM AUTHOR]

Driven by technological advances in various fields (AI, 5G, VR, IoT, etc.) together with the emergence of digital twins technologies (HDT, HAL, BIM, etc.), the Metaverse has attracted growing attention from scientific and industrial communities. This interest is due to its potential impact on people lives in different sectors such as education or medicine. Specific solutions can also increase inclusiveness of people with disabilities that are an impediment to a fulfilled life. However, security and privacy concerns remain the main obstacles to its development. Particularly, the data involved in the Metaverse can be comprehensive with enough granularity to build a highly detailed digital copy of the real world, including a Human Digital Twin of a person. Existing security countermeasures are largely ineffective and lack adaptability to the specific needs of Metaverse applications. Furthermore, the virtual worlds in a large-scale Metaverse can be highly varied in terms of hardware implementation, communication interfaces, and software, which poses huge interoperability difficulties. This paper aims to analyse the risks and opportunities associated with adopting digital replicas of humans (HDTs) within the Metaverse and the challenges related to managing digital identities in this context. By examining the current technological landscape, we identify several open technological challenges that currently limit the adoption of HDTs and the Metaverse. Additionally, this paper explores a range of promising technologies and methodologies to assess their suitability within the Metaverse context. Finally, two example scenarios are presented in the Medical and Education fields. [ABSTRACT FROM AUTHOR]

As social media platforms continue to grow in popularity, ensuring the security and privacy of users' accounts has become a critical concern. Traditional authentication methods, such as passwords and PINs, are vulnerable to various attacks, including password guessing, phishing, and brute force attacks. To address these challenges, researchers have turned to keystroke dynamics as a promising solution. Keystroke dynamics, also known as keystroke biometrics, leverages the unique typing patterns and behavioral characteristics of individuals to authenticate their identities. This approach analyzes various keystroke attributes, such as key press duration, flight time, and typing rhythm, to create a user's unique typing profile. By continuously monitoring and comparing these patterns, social media platforms can verify the authenticity of users in real-time. This paper explores the potential of keystroke dynamics in enhancing security for social media users. It discusses the advantages of this approach, including its non-intrusive nature, continuous authentication capabilities, and resistance to common attacks. Additionally, the paper examines the challenges associated with implementing keystroke dynamics, such as user acceptance, system performance, and potential privacy concerns. [ABSTRACT FROM AUTHOR]

Lesion-symptom mapping studies provide insight into what areas of the brain are involved in different aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is nonuniformly distributed, with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Recent advancements in multiple testing methodologies allow researchers to weigh hypotheses according to side information (e.g., information on power heterogeneity). In this paper, we propose the use of p-value weighting for voxel-based lesion-symptom mapping studies. The weights are created using the distribution of lesion status and spatial information to estimate different non-null prior probabilities for each hypothesis test through some common approaches. We provide a monotone minimum weight criterion, which requires minimum a priori power information. Our methods are demonstrated on dependent simulated data and an aphasia study investigating which regions of the brain are associated with the severity of language impairment among stroke survivors. The results demonstrate that the proposed methods have robust error control and can increase power. Further, we showcase how weights can be used to identify regions that are inconclusive due to lack of power., (© 2024 The Author(s). Biometrical Journal published by Wiley‐VCH GmbH.)

Jabon (Anthocephalus cadamba) is a promising plant species with straight, visually pleasing trunks that can be harvested at a young age as a resource for pulp and paper production due to the rapid growth rate possessed. Therefore, this study aims to investigate the chemical composition and fiber biometry of 3, 6, and 9-year-old Jabon wood, determining their suitability as a pulp and paper raw material. Samples were collected from both stem and branch sections and analyzed according to the Tappi standard. Fiber cell maceration was conducted to enable the precise measurement of fiber dimensions and derivatives. The results showed high cellulose, holocellulose, and lignin content in Jabon wood, with low to moderate levels of extractives and ash. Additionally, the crystallinity index (CrI) increased with age, particularly from 3 to 9 years old. The branch of the plant, with Grade II fiber derivative quality, had lower CrI compared to the stem, while the inner stem showed a lesser value than the outer part. In conclusion, 3-year-old Jabon wood is a potential raw material for pulp and paper production. [ABSTRACT FROM AUTHOR]

Biometrics provide enhanced security and convenience compared to conventional methods of individual authentication. A more robust and effective method of individual authentication has emerged due to recent advancements in multimodal biometrics. Unimodal systems offer lower security and lack the robustness found in multimodal biometric systems. The research paper introduces a novel approach, employing multiple biometric modalities, including face, fingerprint, and iris, to authenticate users in a multimodal biometric system. The paper proposes the "Secure Sense" framework, which combines multiple biometric modalities to improve person verification accuracy. The proposed system utilizes both web-based and real-time datasets. For the web-based dataset, we employed the Chicago Face dataset for facial data, the MMU1 dataset for iris data, and the SOCOfing dataset for fingerprint data. In real-time data collection, facial data is captured using a Zebronics Zeb-Gem webcam, fingerprint data is obtained using the Mantra MFS scanner, and iris data is collected using the Mantra MIS scanner. In the envisioned system, we introduce an innovative approach that employs a decision-level fusion technique across three unique biometric modalities, resulting in an impressive accuracy rate of approximately 93% across all modalities. [ABSTRACT FROM AUTHOR]

In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step p $p$ -value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National Toxicology Program., (© 2024 The Author(s). Biometrical Journal published by Wiley‐VCH GmbH.)

In recent years, both model-based and model-assisted designs have emerged to efficiently determine the optimal biological dose (OBD) in phase I/II trials for immunotherapy and targeted cellular agents. Model-based designs necessitate repeated model fitting and computationally intensive posterior sampling for each dose-assignment decision, limiting their practical application in real trials. On the other hand, model-assisted designs employ simple statistical models and facilitate the precalculation of a decision table for use throughout the trial, eliminating the need for repeated model fitting. Due to their simplicity and transparency, model-assisted designs are often preferred in phase I/II trials. In this paper, we systematically evaluate and compare the operating characteristics of several recent model-assisted phase I/II designs, including TEPI, PRINTE, Joint i3+3, BOIN-ET, STEIN, uTPI, and BOIN12, in addition to the well-known model-based EffTox design, using comprehensive numerical simulations. To ensure an unbiased comparison, we generated 10,000 dosing scenarios using a random scenario generation algorithm for each predetermined OBD location. We thoroughly assess various performance metrics, such as the selection percentages, average patient allocation to OBD, and overdose percentages across the eight designs. Based on these assessments, we offer design recommendations tailored to different objectives, sample sizes, and starting dose locations., (© 2024 Wiley‐VCH GmbH.)

Time-to-event analysis often relies on prior parametric assumptions, or, if a semiparametric approach is chosen, Cox's model. This is inherently tied to the assumption of proportional hazards, with the analysis potentially invalidated if this assumption is not fulfilled. In addition, most interpretations focus on the hazard ratio, that is often misinterpreted as the relative risk (RR), the ratio of the cumulative distribution functions. In this paper, we introduce an alternative to current methodology for assessing a treatment effect in a two-group situation, not relying on the proportional hazards assumption but assuming proportional risks. Precisely, we propose a new nonparametric model to directly estimate the RR of two groups to experience an event under the assumption that the risk ratio is constant over time. In addition to this relative measure, our model allows for calculating the number needed to treat as an absolute measure, providing the possibility of an easy and holistic interpretation of the data. We demonstrate the validity of the approach by means of a simulation study and present an application to data from a large randomized controlled trial investigating the effect of dapagliflozin on all-cause mortality., (© 2024 The Authors. Biometrical Journal published by Wiley‐VCH GmbH.)

A large number of images are available on the Internet with the growth of social networking services, and many of them are face photos or contain faces. It is necessary to protect the privacy of face images to prevent their malicious use by face image de-identification techniques that make face recognition difficult, which prevent the collection of specific face images using face recognition. In this paper, we propose a face image de-identification method that generates a de-identified image from an input face image by embedding facial features extracted from that of another person into the input face image. We develop the novel framework for embedding facial features into a face image and loss functions based on images and features to de-identify a face image preserving its appearance. Through a set of experiments using public face image datasets, we demonstrate that the proposed method exhibits higher de-identification performance against unknown face recognition models than conventional methods while preserving the appearance of the input face images. [ABSTRACT FROM AUTHOR]

Score-driven models have recently been introduced as a general framework to specify time-varying parameters of conditional densities. The score enjoys stochastic properties that make these models easy to implement and convenient to apply in several contexts, ranging from biostatistics to finance. Score-driven parameter updates have been shown to be optimal in terms of locally reducing a local version of the Kullback–Leibler divergence between the true conditional density and the postulated density of the model. A key limitation of such an optimality property is that it holds only locally both in the parameter space and sample space, yielding to a definition of local Kullback–Leibler divergence that is in fact not a divergence measure. The current paper shows that score-driven updates satisfy stronger optimality properties that are based on a global definition of Kullback–Leibler divergence. In particular, it is shown that score-driven updates reduce the distance between the expected updated parameter and the pseudo-true parameter. Furthermore, depending on the conditional density and the scaling of the score, the optimality result can hold globally over the parameter space, which can be viewed as a generalization of the monotonicity property of the stochastic gradient descent scheme. Several examples illustrate how the results derived in the paper apply to specific models under different easy-to-check assumptions, and provide a formal method to select the link function and the scaling of the score. [ABSTRACT FROM AUTHOR]

Facial biometrics are widely used to reliably and conveniently recognize people in photos, in videos, or from real-time webcam streams. It is therefore of fundamental importance to detect synthetic faces in images in order to reduce the vulnerability of biometrics-based security systems. Furthermore, manipulated images of faces can be intentionally shared on social media to spread fake news related to the targeted individual. This paper shows how fake face recognition models may mainly rely on the information contained in the background when dealing with generated faces, thus reducing their effectiveness. Specifically, a classifier is trained to separate fake images from real ones, using their representation in a latent space. Subsequently, the faces are segmented and the background removed, and the detection procedure is performed again, observing a significant drop in classification accuracy. Finally, an explainability tool (SHAP) is used to highlight the salient areas of the image, showing that the background and face contours crucially influence the classifier decision. [ABSTRACT FROM AUTHOR]

Facial recognition systems have emerged as indispensable components in identity verification. These systems heavily rely on facial data, which is stored in a biometric database. However, storing such data in a database raises concerns about privacy breaches. To address this issue, several technologies have been proposed for protecting facial biometrics. Unfortunately, many of these methods can cause irreversible damage to the data, rendering it unusable for other purposes. In this paper, we propose a novel reversible anonymization scheme for face images via cyclic learning. In our scheme, face images can be de-identified for privacy protection and reidentified when necessary. To achieve this, we employ generative adversarial networks with a cycle consistency loss function to learn the bidirectional transformation between the de-identified and re-identified domains. Experimental results demonstrate that our scheme performs well in terms of both de-identification and reidentification. Furthermore, a security analysis validates the effectiveness of our system in mitigating potential attacks. [ABSTRACT FROM AUTHOR]

This paper suggests novel cancellable biometric realization approach recognition and template protection. In this paper, the Homomorphic Filtering Masking (HFM) encoding algorithm is utilized for cancelable palmprint recognition system. In the suggested technique, the Discreet Wavelet Transform (DWT) algorithm is applied on the palmprint. Then the DWT divides the image into four sub images. The resultant map is encrypted with the HFM algorithm, in order to the second HFM utilized in produced from the palmprint. This approach can be used to develop a frequency domain procedure for making this system for biometric template protection. The obtained results prove that the suggested technique is the best from the other algorithms. [ABSTRACT FROM AUTHOR]

To efficiently allocate medical resources at disaster sites, medical workers perform triage to prioritize medical treatments based on the severity of the wounded or sick. In such instances, evaluators often assess the severity status of the wounded or sick quickly, but their measurements are qualitative and rely on experience. Therefore, we developed a wearable device called Medic Hand in this study to extend the functionality of a medical worker's hand so as to measure multiple biometric indicators simultaneously without increasing the number of medical devices to be carried. Medic Hand was developed to quantitatively and efficiently evaluate "perfusion" during triage. Speed is essential during triage at disaster sites, where time and effort are often spared to attach medical devices to patients, so the use of Medic Hand as a biometric measurement device is more efficient for collecting biometric information. For Medic Hand to be handy during disasters, it is essential to understand and improve upon factors that facilitate its public acceptance. To this end, this paper reports on the usability evaluation of Medic Hand through a questionnaire survey of nonmedical workers., (© 2024. The Author(s).)

This paper presents a novel multitask learning framework for palmprint biometrics, which optimizes classification and hashing branches jointly. The classification branch within our framework facilitates the concurrent execution of three distinct tasks: identity recognition and classification of soft biometrics, encompassing gender and chirality. On the other hand, the hashing branch enables the generation of palmprint hash codes, optimizing for minimal storage as templates and efficient matching. The hashing branch derives the complementary information from these tasks by amalgamating knowledge acquired from the classification branch. This approach leads to superior overall performance compared to individual tasks in isolation. To enhance the effectiveness of multitask learning, two additional modules, an attention mechanism module and a customized gate control module, are introduced. These modules are vital in allocating higher weights to crucial channels and facilitating task-specific expert knowledge integration. Furthermore, an automatic weight adjustment module is incorporated to optimize the learning process further. This module fine-tunes the weights assigned to different tasks, improving performance. Integrating the three modules above has shown promising accuracies across various classification tasks and has notably improved authentication accuracy. The extensive experimental results validate the efficacy of our proposed framework.

Biometrics is a field that has been given importance in recent years and has been extensively studied. Biometrics can use physical and behavioural differences that are unique to individuals to recognize and identify them. Today, biometric information is used in many areas such as computer vision systems, entrance systems, security and recognition. In this study, a new biometrics database containing silhouette, thermal face and skeletal data based on the distance between the joints was created to be used in behavioural and physical biometrics studies. The fact that many cameras were used in previous studies increases both the processing intensity and the material cost. This study aimed to both increase the recognition performance and reduce material costs by adding thermal face data in addition to soft and behavioural biometrics with the optimum camera. The presented data set was created in accordance with both motion recognition and person identification. Various data loss scenarios and multi-biometrics approaches based on data fusion have been tried on the created data sets and the results have been given comparatively. In addition, the correlation coefficient of the motion frames method to obtain energy images from silhouette data was tested on this dataset and yielded high-accuracy results for both motion and person recognition., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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.)

Face biometrics play a crucial role in describing gender and age changes. Male and female faces differ in terms of smoothness due to the formative properties, nature of lifestyle, and use of cosmetics. For age progression, faces record different levels of face vitality - from smooth face within childhood to wrinkles and folds in senior adult faces. This paper presents a set of dispersion measures to represent face features over gender and age variation. Besides, we inspect the exchanging effects between gender differences and life stages on the yielded results. These dispersion features are represented using localized formulas of statistical measures, such as Standard Deviation (SD), Statistical Ratio (SR) and Roughness Value (RV). Such statistical measures inspect the differences of image values regarding specific reference. The SD inspects the differences between a set of values and their average, while SR gauges the SD for set of values normalized by their average. The RV measure inspects the difference between the successive values normalized by their differences from the average. This paper adopts the localized forms of these measures to avoid the effects of the challenges (illumination and rotation) that dataset images suffer from. These adopted features provide significant discriminating powers over different ages and genders. The yielded results show that the adopted measures recorded mutual effects between age estimation and gender recognition. [ABSTRACT FROM AUTHOR]

Accurate object tracking in low-light environments is crucial, particularly in surveillance, ethology applications, and biometric recognition systems. However, achieving this is significantly challenging due to the poor quality of captured sequences. Factors such as noise, color imbalance, and low contrast contribute to these challenges. This paper presents a comprehensive study examining the impact of these distortions on automatic object trackers. Additionally, we propose a solution to enhance the tracking performance by integrating denoising and low-light enhancement methods into the transformer-based object tracking system. Experimental results show that the proposed tracker, trained with low-light synthetic datasets, outperforms both the vanilla MixFormer and Siam R-CNN. [ABSTRACT FROM AUTHOR]

The Great Knot is a species from the Scolopacidae family of waders migrating within the East Asian-Australasian Flyway, and studies on this species have contributed greatly to understanding of migration ecophysiology and migration strategies in long-distance migrants. In this paper, we provide the first description of biometric variability and sexual size dimorphism in Great Knots. During the study on the Kamchatka Peninsula, 683 adults and 229 juveniles were measured and sexed molecularly. In adults, the mean measurements of females were larger than in males, except for tarsus length. In juveniles, at the early stage of migration from breeding to wintering grounds, apparently growth was not complete. Sexual dimorphism was small, with only wing length being significantly longer in females than in males. All dimensions of juveniles were smaller than those of adults, especially in bill length. The most sexually dimorphic trait in both adults and juveniles was wing length, and the most effective discriminant function with wing length as a single predictor correctly identified the sex of 76% of birds in both age classes. However, molecular sexing is the method of choice for reliable sexing, especially in juveniles. [ABSTRACT FROM AUTHOR]

Biometric authentication methods offer high-quality mechanisms to confirm the identity of individuals in security systems commonly used in the modern world, such as physical access control, online banking, or mobile device unlocking. They also find their application in cryptographic solutions, which allow the biometrically authenticated exchange of cryptographic keys between users and services on the internet, despite the fuzziness of biometric data. Such solutions are BAKE (biometrics-authenticated key exchange) and BRAKE (biometric-resilient authenticated key exchange) protocols, upon which our work is based. However, the direct application of fuzzy biometrics in cryptography, which relies heavily on the accuracy of single-bit secret values, is not trivial. Therefore, this paper is devoted to analyzing the security of this idea and the feasibility of implementing biometric AKE (authenticated key exchange) protocols, with an emphasis on the BRAKE protocol. As the results of our analysis, we discuss BRAKE's limitations and vulnerabilities, which need to be appropriately addressed to implement the protocol in modern systems. [ABSTRACT FROM AUTHOR]

Humanitarian organizations are increasingly using biometric technology. Although the existing literature comprehensively covers this issue, it overlooks the considerable divergence in approaches and policies. In fact, there are significant differences in how biometric data are collected, stored, processed, shared, and protected. Drawing on an analysis of relevant news items, documents, and 17 semi‐structured interviews with humanitarian practitioners, this paper explains the variational biometric practices of humanitarian organizations. The case studies are the UN Refugee Agency, the United Nations World Food Programme, the International Committee of the Red Cross, Médecins Sans Frontières, and World Vision International. Contributing to the scholarly debates about, first, the use of technology in humanitarian response and, second, the complex interdependence and power relations between major donors, States, and international organizations, this paper argues that combinations of three broad factors – organizational culture, mandates and scope of action, and institutional structure significantly shape how each organization understands and practices biometrics. Humanitarian organizations have different interests, motivations, ideologies, statuses, structures, and areas of expertise, and they respond differently to the demands of the States and other influential actors, thus having different applications of biometric data. [ABSTRACT FROM AUTHOR]

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 Transformations in Business & Economics is the property of Vilnius University 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.)

Accurate and appropriate pain assessment is crucial for effective pain treatment and management. When patients who are nonverbal cannot communicate, observational pain assessment carried out by health providers is riddled with issues of bias and subjectivity. Artificial intelligence-driven facial recognition technologies (FRTs) are proposed as the solution to the challenges prevalent in observational pain assessment of nonverbal patients by care providers. It promises to streamline, objectify, and promote accuracy to mitigate inaccurate pain assessment in nonverbal patients. Inaccurate pain assessment could lead to undertreatment or overtreatment of pain in nonverbal patients, with far-reaching and unintended consequences. This paper argues that close ethical scrutiny is pertinent for the deployment of AI-driven FRT for pain assessment to mitigate bias and unequal outcomes in nonverbal patients. A concerted effort by medical professionals is expedient to assure the integration of transparent, explainable, and robust data-driven FRT for pain assessment in the clinical setting to promote quality care and equitable outcomes in nonverbal patients. [ABSTRACT FROM AUTHOR]

Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device's modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements. [ABSTRACT FROM AUTHOR]

Humanitarian organisations are increasingly utilising biometric data. However, we know little about the extent and scope of this practice, as its benefits and risks have attracted all the attention so far. This paper explores the biometric practices of the United Nations Refugee Agency, the United Nations World Food Programme, the International Committee of the Red Cross, Médecins Sans Frontières, and World Vision International. The study analysed relevant documents published over the past two decades and 17 semi‐structured interviews with humanitarian workers conducted between June 2021 and June 2022. The findings reveal that humanitarian organisations use diverse types and functions of biometric data for different services, collaborate with many actors, and employ various data protection measures. Ultimately, challenging the straightforward generalisations about the use of such data, the paper argues that variational applications of biometrics in the humanitarian context require case‐by‐case analysis, as each instance will likely produce a different outcome. [ABSTRACT FROM AUTHOR]

Recently, biometric recognition has become a significant field of research. The concept of cancelable biometrics (CB) has been introduced to address security concerns related to the handling of sensitive data. In this paper, we address unconstrained face verification by proposing a deep cancelable framework called BiometricNet+ that employs random projections (RP) to conceal face images and compressive sensing (CS) to reconstruct measurements in the original domain. Our lightweight design enforces the properties of unlinkability, revocability, and non-invertibility of the templates while preserving face recognition accuracy. We compare facial features by learning a regularized metric: at training time, we jointly learn facial features and the metric such that matching and non-matching pairs are mapped onto latent target distributions; then, for biometric verification, features are randomly projected via random matrices changed at every enrollment and query and reconstructed before the latent space mapping is computed. We assess the face recognition accuracy of our framework on challenging datasets such as LFW, CALFW, CPLFW, AgeDB, YTF, CFP, and RFW, showing notable improvements over state-of-the-art techniques while meeting the criteria for secure cancelable template design. Since our method requires no fine-tuning of the learned features, it can be applied to pre-trained networks to increase sensitive data protection. [ABSTRACT FROM AUTHOR]

In the realm of cloud computing, ensuring robust data security is of utmost importance. Authentication, a cornerstone of safeguarding information, continuously evolves to counter escalating threats. This paper introduces MultiFusionGuard, an innovative multimodal biometric authentication framework aimed at bolstering cloud security. By leveraging diverse biometric traits like fingerprints, iris scans, and palm prints, MultiFusionGuard utilizes their unique patterns to enhance security measures. Each trait undergoes meticulous image processing stages, encompassing pre-processing, normalization, and feature extraction. The fusion of these distinct biometric features at multiple levels results in a robust authentication framework. Integration of these features establishes a comprehensive and intricate authentication system, thereby amplifying the complexity and effectiveness of security measures. The system's efficacy is evaluated using indicators such as incorrect rejection ratio, incorrect acceptance ratio, and execution time to ensure reliability and resilience against illegal access attempts. MultiFusionGuard offers a promising solution to fortify data protection within cloud environments, providing advanced defense mechanisms against potential security breaches. [ABSTRACT FROM AUTHOR]

The ear is a visible organ with a unique structure for each person. As a result, it can be used as a biometric to circumvent the constraints of person identification. Deep learning methods like You Only Look Once (YOLO) and MobileNet have recently significantly aided real-time biometric recognition. As a result, in this paper, we approach identifying a person using YOLOV3, YOLOV5, MobileNet-SSDV1, and MobileNet-SSDV2 deep learning algorithms using their ear biometrics. The used ear biometric is a standard dataset (EarVN1.0 Dataset) from 164 individuals with a total of 27,592 images. We chose 10 people at random, totaling 2057 pictures. Of these, 85% were used for training, 5% for validation, and 10% for testing. The performance of the algorithms is determined based on their accuracy and how smoothly the ear of a person is detected. The training accuracy of the algorithms is thresholded at 99.87%. MobileNet-SSDV1, MobileNet-SSDV2, YOLOV3, and YOLOV5 have testing accuracy that is 88%, 91%, 95%, and 96%, respectively. We concluded that the YOLOV5 model outperforms the others in terms of accuracy and size (16MB) for person identification using ear biometrics. [ABSTRACT FROM AUTHOR]

The rapid growth of smart technologies and services has intensified the challenges surrounding identity authentication techniques. Biometric credentials are increasingly being used for verification due to their advantages over traditional methods, making it crucial to safeguard the privacy of people's biometric data in various scenarios. This paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric systems. It proposes a noble and thorough taxonomy survey for privacy-preserving techniques, as well as a systematic framework for categorizing the field's existing literature. We review the state-of-the-art methods and address their advantages and limitations in the context of various biometric modalities, such as face, fingerprint, and eye detection. The survey encompasses various categories of privacy-preserving mechanisms and examines the tradeoffs between security, privacy, and recognition performance, as well as the issues and future research directions. It aims to provide researchers, professionals, and decision-makers with a thorough understanding of the existing privacy-preserving solutions in biometric recognition systems and serves as the foundation of the development of more secure and privacy-preserving biometric technologies. [ABSTRACT FROM AUTHOR]

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]

The following paper presents a solution to the problem of offline recognition of Japanese characters. Minutiae and other features extractable from handwriting images have been used to recognize individual characters. The solution presented by the authors uses minutiae to recognise single Japanese characters. Due to the complexity of this typeface, the solution presented can be used to recognise archaic characters, from old documents or also works of art. Neural Networks and hybrid classifiers based on five basic types of classifiers, i.e., k-nearest neighbour method, decision trees, support vector machine, logistic regression and Gaussian Naive Bayes classifier have been developed for classification. The study was conducted on Hiragana, Katakana and Kanji characters (ETL9G Database). The accuracy value obtained was 99.934%. The authors present what is probably the first algorithm using minutiae to recognize Japanese handwriting. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in biometric recognition based on finger dorsal patterns, making it a significant area of research. This paper introduces a biometric classification system that utilizes dorsal finger middle creases. The viability of this trait is assessed through the implementation of a new method known as multi block circular shift combination local binary pattern (MBCSC-LBP). The MBCSC-LBP approach involves dividing the image into multiple blocks to enhance robustness and capture both local and global information, thereby extracting discriminative features. These features from each block are then concatenated to form a comprehensive feature vector. To evaluate the accuracy of the proposed MBCSC-LBP feature extractor, a support vector machine (SVM) with a linear kernel is utilized. The classification accuracy achieved by this method is 96.22% indicating a promising performance. [ABSTRACT FROM AUTHOR]

In today's digital era, ensuring robust and secure authentication mechanisms is crucial. Multi-fusion biometric authentication systems have emerged as a powerful solution to enhance security and reliability by integrating multiple biometric traits. This paper presents a novel Multi-Fusion Biometric Authentication approach using Minutiae-Driven Fixed-Size Template Matching (MFTM). The proposed method leverages the unique features of minutiae points in fingerprints and combines them with other biometric modalities, such as iris and facial recognition, to create a fixed-size template for matching. The fusion process involves extracting and normalizing minutiae points from the fingerprint, followed by their integration with iris and facial features using a robust feature fusion algorithm. The fixed-size template ensures consistency and efficiency in the matching process, addressing challenges related to template size variability and computational overhead. Extensive experiments conducted on standard biometric datasets demonstrate that the proposed MFTM approach significantly enhances authentication accuracy, reduces false acceptance and rejection rates, and provides a highly secure and scalable authentication solution suitable for various applications, including access control and identity verification. The results show an authentication accuracy of 98.7%, a false acceptance rate (FAR) of 0.2%, and a false rejection rate (FRR) of 0.5%. Additionally, the computational time for matching is reduced by 25% compared to traditional methods, highlighting the efficiency and practicality of the proposed approach. [ABSTRACT FROM AUTHOR]

The Later Iron Age in Britain was a transformative period: material culture, settlement patterns, technology, trade networks, and the structures of power changed, ultimately leading to the Roman invasion. This paper examines the significance of investigating animal economies in this period within the broader context of socio‐economic developments. It reviews the available evidence regarding animal economies in this period, integrates new osteometric analyses, and discusses diachronic changes using the Roman evidence on a comparative basis. The investigation shows a broad pattern of continuity of practice, with relatively uniform livestock types and management strategies until the very end of the Iron Age. This suggests that the trajectory of local farming practices was largely independent from Mediterranean developments. This study contributes to a deeper understanding of Iron Age societies and their response to external influences, while also informing future research directions in archaeology. [ABSTRACT FROM AUTHOR]