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Forensic personal identification using facial recognition systems is of scientific interest due to the rapid digitalization of society and the necessity to quickly identify the perpetrator. The implementation of advanced technical solutions in forensics allows to enrich the theoretical basis, which directly affects the effectiveness of countering crimes, including terrorist and extremist crimes. Purpose: to examine the value and capabilities of facial image biometric identification systems for the detection and investigation of crimes. Methods: to achieve the set purpose, dialectical, systemic and logical methods (general scientific) are used. Among specific scientific research methods, formal legal methods, methods of generalization and abstraction prevailed. Results: the issues of legal regulation of the use of biometric data in the process of personal identification are considered; the possibilities of using face recognition systems installed in public places are studied using the example of large cities in Russia. Ways are proposed to increase the efficiency of biometric identification using a facial image in the context of countering crimes.

This research designs a touchless fingerprint identification biometric system. This research problem stems from the user's need to change the conventional system to touchless in the hope of minimizing direct contact that can be scanned by someone who is not interested. This research aims to implement palm images in validating identity using GLCM (Gray-Level Co-occurrence Matrix) features with the K-Nearest Neighbours (K-NN) classification method in MATLAB. The identification process is divided into several stages: image acquisition, pre-processing, feature extraction with GLCM, and database matching using KNN classification. System testing uses the 10-fold cross validation method with 100 image samples (90 training images and 10 test images) that are tested in turn to calculate the average accuracy and analyze system performance. Furthermore, the test used GLCM angles (0o, 45o, 90o dan 135o) and K-NN with k values of 1, 5 and 7. The results showed the highest accuracy of 72% using an angle of 0° in GLCM and k=1 and the lowest at an angle of 90o and k=7 in K-NN. The advantage of this design is the recognition of the identity of the fingerprint owner in real time.

The palmprint recognition model has been attracting researchers in the last few years. Among the mage concentrations, most of the researchers focused on local feature extraction model and recognition model, or a few focused on global feature extraction model for classification purposes. In this model, the main focus was on fusion of both the local and global features of palmprint image. Various traditional methods were used to extract both the global and local features of palmprint images. The feature selection algorithm selects the optimal features from a list to include in fusion. Hausdorff distance method is used for matching purpose. For this model, two different datasets (IITD and Tongji palmprint) were used. The accuracy of the dataset was 98.91% and 98.25%, respectively. Both datasets are contactless. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are vulnerable to threats due to wireless data transmission. However, IoT devices are resource- and energy-constrained, so building lightweight security that provides stronger authentication is essential. This paper proposes a novel, two-layered multi-factor authentication (2L-MFA) framework using blockchain to enhance IoT devices and user security. The first level of authentication is for IoT devices, one that considers secret keys, geographical location, and physically unclonable function (PUF). Proof-of-authentication (PoAh) and elliptic curve Diffie–Hellman are followed for lightweight and low latency support. Second-level authentication for IoT users, which are sub-categorized into four levels, each defined by specific factors such as identity, password, and biometrics. The first level involves a matrix-based password; the second level utilizes the elliptic curve digital signature algorithm (ECDSA); and levels 3 and 4 are secured with iris and finger vein, providing comprehensive and robust authentication. We deployed fuzzy logic to validate the authentication and make the system more robust. The 2L-MFA model significantly improves performance, reducing registration, login, and authentication times by up to 25%, 50%, and 25%, respectively, facilitating quicker cloud access post-authentication and enhancing overall efficiency. [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]

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Different kinds of occlusion have proven to disrupt the accuracy of face recognition systems, one of them being masks. The problem of masked faces has become even more apparent with the widespread of the COVID-19 virus, with most people wearing masks in public. This brings up the issue of existing face recognition systems been able to accurately recognize people even when part of their face and the major identifiers (such as the nose and mouth) are covered by a facemask. In addition, most of the databases that have been curated in different organizations, countries are majorly of non-masked faces, and masked databases are rarely stored or universally accepted compared with conventional face datasets. Therefore, this paper aim at the development of a Masked Face Recognition System using facial anthropometrics technique (FAT). FAT is the science of calculating the measurements, proportion and dimension of human face and their features. A dataset of faces with individual wearing medical face mask was curated. Using the Facial anthropometry based technique a Masked Face Recognition System developed. This system was implemented using Local Binary Patterns Histogram algorithms for recognition. On testing the developed system trained with unmasked dataset, show a high recognition performance of 94% and 96.8% for masked and non-masked face recognition respectively because of the Facial anthropometry based technique adapted. On deployment, users were been recognized when they are wearing a mask with part of their face covered in real-time. [ABSTRACT FROM AUTHOR]

The widespread use of iris recognition systems has led to a growing demand for enhanced security measures to counter potential iris presentation attacks, also known as anti-spoofing. To enhance the security and reliability of iris recognition systems, researchers have developed numerous methods for detecting presentation attacks. Most of these methods lack precision in detecting unknown attacks compared to known attacks. In addition, most literature on iris presentation attack detection (PAD) systems utilizes near-infrared (NIR) samples as inputs. These samples produce superior-quality and robust images with less reflection in the cornea of the eye. Despite this, due to the widespread use of smartphones and the necessity for unsupervised identity verification, visible-light samples play a crucial role in detecting presentation attacks. These samples can be easily captured using smartphone cameras. In this paper, a dual-spectral attention model has been developed to train a unified model for multiple real-world attack scenarios. Two different scenarios were tested. In the first scenario, the model was trained as a one-class anomaly detection (AD) approach, while in the second scenario, it was trained as a normal two-class detection approach. This model achieved the best result for the attack presentation classification error rate (APCER) of 4.87% in a one-class AD scenario when tested on the attack dataset, outperforming most studies on the same test dataset. These experimental results suggest that future research opportunities in areas such as working with visible light images, using an AD approach, and focusing on uncontrolled environment samples and synthetic iris images may improve iris detection accuracy [ABSTRACT FROM AUTHOR]

Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms' fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels, and in the contactless fingerprint sensor setting, 3D printing and silicone filling provide the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment. [ABSTRACT FROM AUTHOR]

Gait recognition is one of the most promising video-based biometric technologies. The edge of silhouettes and motion are the most informative feature and previous studies have explored them separately and achieved notable results. However, due to occlusions and variations in viewing angles, their gait recognition performance is often affected by the predefined spatial segmentation strategy. Moreover, traditional temporal pooling usually neglects distinctive temporal information in gait. To address the aforementioned issues, we propose a novel gait recognition framework, denoted as GaitASMS, which can effectively extract the adaptive structured spatial representations and naturally aggregate the multi-scale temporal information. The Adaptive Structured Representation Extraction Module (ASRE) separates the edge of silhouettes by using the adaptive edge mask and maximizes the representation in semantic latent space. Moreover, the Multi-Scale Temporal Aggregation Module (MSTA) achieves effective modeling of long-short-range temporal information by temporally aggregated structure. Furthermore, we propose a new data augmentation, denoted random mask, to enrich the sample space of long-term occlusion and enhance the generalization of the model. Extensive experiments conducted on two datasets demonstrate the competitive advantage of proposed method, especially in complex scenes, i.e., BG and CL. On the CASIA-B dataset, GaitASMS achieves the average accuracy of 93.5% and outperforms the baseline on rank-1 accuracies by 3.4% and 6.3%, respectively, in BG and CL. The ablation experiments demonstrate the effectiveness of ASRE and MSTA. The source code is available at https://github.com/YanSun-github/GaitASMS. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) 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.)

This paper aims at developing an intelligent face recognition students' attendance system to enhance school attendance tracking. The system is made up of four phases-database of students' details, face detection, face recognition and attendance report. The database stores all the students' details and images of the students' face captured while face detection and recognition is carried out with convolutional neural network algorithms from the face recognition and opencv library. As faces are detected and recognized from live streaming video of the classroom, attendance are recorded into an excel file and then sent to a real time database. The system also contains a mobile interface through which the course instructors can access information at all time. The methodology adopted for this work is object-oriented analysis and design methodology (OOADM) while programming language used is Python. This work has helped immensely to eliminate the issue of proxy attendance as well as reduce the time wasted in manual attendance system. It is very beneficial to schools and other institutions where attendance is required. [ABSTRACT FROM AUTHOR]

This research paper illustrates about how to manage attendance using a new method that combines face detection techniques together with Python programming as well as machine learning methods among others; which are CNN models. The paper gives details on data collection methods as well as pre-processing methods other than explaining the architecture of CNN. According to the results obtained from its experiments it could be concluded that this system is capable of accurately identifying individuals and recording their presence or absence thus may contribute to increasing productivity in schools, businesses, or other organisations. [ABSTRACT FROM AUTHOR]

Facial recognition technologies (FRTs) are used by law enforcement agencies (LEAs) for various purposes, including public security, as part of their legally mandated duty to serve the public interest. While these technologies can aid LEAs in fulfilling their public security responsibilities, they pose significant risks to data protection rights. This article identifies four specific risks associated with the use of FRT by LEAs for public security within the frameworks of the General Data Protection Regulation and Artificial Intelligence Act. These risks particularly concern compliance with fundamental data protection principles, namely data minimisation, purpose limitation, data and system accuracy, and administrative challenges. These challenges arise due to legal, technical, and practical factors in developing algorithms for law enforcement. Addressing these risks and exploring practical mitigations, such as broadening the scope of data protection impact assessments, may enhance transparency and ensure that FRT is used for public security in a manner that serves the public interest.

This paper examines transformations undertaken by the Malaysian government in reforming its deportation policy, operation, and enforcement. It focuses on the post-2008 period during which Malaysia embarked on several reform initiatives, notably the introduction of biometric technology, the implementation of the National Blue Ocean Strategy, and the establishment of a National Task Force under military leadership. This paper aims to analyse the implementation of the reforms and the implications of these initiatives. The analysis draws upon parliamentary debates, ministerial documents, legal texts, online news media and secondary literature. This paper has found the following three findings. First, deportation could be conceptualised as a migration control strategy to achieve zero irregularity. Second, Malaysia’s deportation regime is increasingly technologically driven, integrated, and militarised. Third, Malaysia has established a network of border and migration management databases centred on the identification, monitoring, and surveillance of individuals. The integration of biometrics technology in interior enforcement has led o the emergence of a digital border.

A person is automatically recognized in a biometric system by analyzing their distinct traits. Most people agree that iris recognition is the most accurate and dependable biometric identification method currently in use. The goal is to accurately and efficiently identify a person in real time by analyzing the sporadic patterns seen in the iris if an eye from some distance, by extracting strong features using deep learning technique. The extracting of significant features is important step that effect the overall accuracy of iris recognition system. In order to extract the iris characteristics, this research suggests a robust convolution neural network (CNN) structure. Then, an identity of the person is determine based on extracted features from his iris to support vector machine (SVM). The proposed system is examined on CASIA V. (1). Several parameters such as accuracy, precision, recall, and F-score are computed to evaluate the performance of the proposed system. The obtained result of the accuracy is about (99%). The proposed system results are compared with several previous methods and prove its effectiveness. A contemporary approach and the suggested method are contrasted and use deep learning for features extraction and the results depict our method outperforms other methods.

Speech remains a prevalent mode of communication powering various intelligent functions in human-computer interaction applications, notably in Speaker/Person Identification (PID) systems. However, there is a considerable population of Non-speaking and Minimal-speaking (NMS) individuals, who heavily rely on nonverbal vocalizations for communication, and the existing speech-based PID systems may not be suitable for users from this community. This study delves into the use of nonverbal vocalizations to identify NMS subjects, termed as NMS-PID, and explores the feasibility of developing an identification system, namely S-NMS-PID, that accommodates both speaking users (with speech input) and NMS users (with nonverbal-vocalization input). Leveraging the recently published ReCANVo dataset of NMS nonverbal vocalizations and our speech dataset, our experiments with multiple networks and acoustic features demonstrate promising results for NMS-PID and S-NMS-PID, evident in average accuracies ranging from 70% to 92%. The proposed convolutional recurrent neural network-based model, despite its smaller size, achieves results nearly on par with much deeper models such as VGG16 and ResNet50. Our findings also highlight the efficacy of Mel-frequency cepstral coefficients features compared to the spectrogram features. Furthermore, a two-step training strategy involving supervised contrastive learning for representation learning followed by fine-tuning with cross-entropy loss significantly enhances robustness and accuracy, particularly in classifying data from minority classes, enhancing overall performance. This study’s outcomes hold potential for tailoring human-computer interaction applications specifically for NMS users. Implementing NMS-PID and S-NMS-PID in security and authentication processes ensures secure and reliable user identification across diverse platforms, transcending sole reliance on speech-based methods.

Fingerprint-based biometric systems are widely used because of their advantages against conventional authentication systems based on passwords and tokens. However, a major limitation is that individuals’ fingerprint information cannot be easily changed if compromised. The fuzzy vault is a promising technique that secures fingerprint data by generating a set of data from the fingerprint using an injective function, preventing the original fingerprint from being regenerated. Nevertheless, the fingerprint fuzzy vault is computationally intensive and requires substantial memory resources. We propose enhancing the performance of fingerprint fuzzy vaults and reducing resource consumption using a new chaff point generation technique based on square boundaries and composite representation. We conducted integration testing along with detailed benchmarking of the fingerprint fuzzy vault using square-boundary generation against other techniques proposed in the literature for each stage. The experiments demonstrate that our proposal yields relatively better results in terms of False Rejection Rate, False Acceptance Rate, computational time, the number of chaff points generated, and memory usage.

Writing Mandarin characters is considered the most challenging component for beginners due to the rules and character formations. This paper explores the potential of a machine learning-based digital learning tool to write Mandarin characters. It also conducts a comparative study between MobileNetV2 and MobileNetV3, exploring different configurations. The research follows the Multimedia Development Life Cycle (MDLC) method to create both application and machine learning models. Participants from higher education institutions that offer Mandarin courses in Batam, Indonesia, participated in a User Acceptance Test (UAT). Data were collected through questionnaires and analyzed using the System Usability Scale (SUS) methods. The results show positive user acceptance, with an SUS score of 77.92%, indicating a high level of acceptability. MobileNetV3Small was also preferred for recognizing user handwriting, due to comparable accuracy size, rapid inference time and smallest model size. Although the application was well received, several participants provided constructive feedback, suggesting potential improvements.

The importance of digital security in today's technological era requires various innovations in creating a reliable security system for humans. Biometrics is an authentication method and the most effective system for performing personal recognition because biometrics have unique characteristics. Dorsal hand vein become biometrics for the individual recognition process in this study using feature extraction of gabor filters and neural network backpropagation to classify recognition into five classes of human individuals, which are expected to be able to provide a higher accuracy value when compared to research on the introduction of dorsal hand vein. This classification process has several stages, namely input image, image pre-processing, segmentation, feature extraction, and image classification. The test results show that the percentage of success based on the five test scenarios has an average value of 75%. In this study, the results of the greatest test accuracy in the fourth scenario were 91%.

In recent years, biometrics has become widely adopted in access control systems, effectively resolving the challenges associated with password management in identity authentication.However, traditional biometric-based authentication methods often lead to the loss or leakage of users’ biometric data, compromising the reliability of biometric authentication.In the literature, two primary technical approaches have been proposed to address these issues.The first approach involves processing the extracted biometric data in a way that the authentication information used in the final stage or stored in the database does not contain the original biometric data.The second approach entails writing the biometric data onto a smart card and utilizing the smart card to generate the private key for public key cryptography.To address the challenge of constructing the private key of a public key cryptosystem based on fingerprint data without relying on a smart card, a detailed study was conducted on the stable feature points and stable feature distances of fingerprints.This study involved the extraction and analysis of fingerprint minutiae.Calculation methods were presented for sets of stable feature points, sets of equidistant stable feature points, sets of key feature points, and sets of truncated means.Based on the feature distance truncated mean, an original fingerprint key generation algorithm and key update strategy were proposed.This scheme enables the reconstruction of the fingerprint key through re-collecting fingerprints, without the need for direct storage of the key.The revocation and update of the fingerprint key were achieved through a salted hash function, which solved the problem of converting ambiguous fingerprint data into precise key data.Experiments prove that the probability of successfully reconstructing the fingerprint key by re-collecting fingerprints ten times is 0.7354, and the probability of reconstructing the fingerprint key by re-collecting fingerprints sixty times is 98.06%.

The electrocardiogram is a topic of great importance from a medical and biometric perspective, especially recently, as researchers have begun to search for new biometric methods other than the palm print, fingerprint, or iris as alternative systems. Researchers discovered that ECG has unique features that are not common among humans, making it a good topic for researchers in biometric systems for identifying people. In this research paper, the goal is to shed light on the most important basic concepts that are related to ECG in terms of the methods used by researchers and in terms of the most critical data sets used by researchers, and also to shed light on some previous studies that achieved a high rate of citations, and also to shed light on the most important basic concepts that make Its features are unique and intelligence methods can be used effectively.

Person recognition systems have been applied for several years, as fingerprint recognition has been experimented with different image resolutions for 15 years. Fingerprint recognition and biometrics for security are becoming commonplace. Biometric systems are emerging and evolving topics seen as fertile ground for researchers to investigate more deeply and discover new approaches. Among the most prominent of these systems is the palm printing system, which identifies individuals based on the palm of their hands because of the advantages that the palm possesses that cannot be replicated among humans, as in its theory of other fingerprints. This paper proposes a biometric system to identify people by handprint, especially palm area, using deep learning technology via a pre-trained model on the PolyU-IITD dataset. The proposed system goes through several basic stages, namely data pruning, processing, training, and prediction, and the results were promising, as the system's accuracy reached 90% based on the confusion matrix measures.

Electrocardiogram (ECG) signals are a very important tool for clinical diagnosis and can be used as a new biometric modality. The aim of this research is to determine the results of ECG signal processing using RNN methods such as the Long Short Term Memory (LSTM) algorithm by utilizing several preprocessing techniques. In this study, the ECG signal itself was previously tested by carrying out the LSTM classification process without preprocessing, and the results obtained were 0% accurate, so preprocessing was needed. The preprocessing methods tested with the LSTM classification method are Adjacent Segmentation and R Peak Segmentation to find out which preprocessing techniques greatly influence LSTM classification accuracy. The experimental results were that LSTM classification with R Peak Segmentation preprocessing obtained the highest accuracy on the two data used, namely filtered and raw data, with 80.7% and 78.95%, respectively. Meanwhile, the accuracy obtained from LSTM classification when using Adjacent Segmentation preprocessing is not good. This research compares LSTM accuracy from each preprocessing stage to determine which combination has the best results in the ECG data classification process. This research also offers new insights into the preprocessing stages that can be carried out on ECG data.

In recent years, security systems based on biometric features have become a promising solution to identify humans, and it is preferred over traditional methods working based on what we know. With the rapid growth of such identification methods, ECG authentication approaches as an emerging biometric recognition scheme is developed. It can be efficiently used to identify individuals, specifically for continuous authentication to allow particular access privileges for users. In comparison with other biometric features even in abnormal conditions, it gives more valid and better results. Although there are several works that have offered some techniques in order to overcome the various issues affecting the ECG authentication schemes' outputs, there are still many concerns to be considered. How can we see, there are not many studies that deal with all aspects of ECG authentication techniques? The objective this paper is to evaluate some surveys related to ECG authentication domain since 2010. We have done a comprehensive taxonomy including existing methods and techniques in ECG based authentication domain. With the aim of providing a classical taxonomy form, this study presents a Systematic Literature Review (SLR) on ECG-based authentication schemes to introduce the state-of-the-art approaches in this domain. We have done the selection of journals and conference proceedings using the standard systematic literature review methodology in order to find and assess the studies related to ECG authentication. Finally, the paper is concluded with a summary of the content of the paper, and open issues and future research challenges are discussed. [ABSTRACT FROM AUTHOR]

The mangrove oyster (Crassostrea) is one of the most common bivalves found on the Brazilian coast, widely used in artisanal fisheries. Oysters are dioecious with external reproduction, typically found in estuarine environments. The present study aimed to identify the population aspects of Crassostrea in different fishing locations in northeastern Brazil. The study area was the Acaú-Goiana Extractive Reserve, a protected area focused on the development of artisanal fisheries. Oysters were collected during low tide at three sampling points, where their removal from mangrove roots was carried out, during May, July, September, November 2021, and January and March 2022. In the laboratory, biometric measurements were conducted, and for the months of May and November 2021 histological preparations to study the reproductive cells. Biometric measurements were significantly higher in the dry season at all sampling points. The sex ratio was M = 0.38, F = 0.62, for May, and M = 0.30, F = 0.69, for November. The biometric and reproductive data obtained can support the development of management strategies, including a rotation plan between collection sites and the establishment of a minimum size for fishing. Here we recommend a minimum size of 20 mm shell length (SL) or larger for fishing. [ABSTRACT FROM AUTHOR]

In this technological era, as the need for security arises, the use of biometrics is increasing in authentication systems as a secure and convenient method of human identification and verification. Electroencephalogram (EEG) signals have gained significant attention among the various biometric modalities available because of their unique and unforgeable characteristics. In this study, we have proposed an EEG‐based multi‐subject and multi‐task biometric authentication system for the military applications that address the challenges associated with multi‐task variation in EEG signals. The proposed work considers the use of respective EEG signals for the access of artillery, entrance to highly confidential places for the military and so forth by authenticated personnel only. We have used a multi‐subject, multi‐session, and multi‐task (M3CV$$ {M}^3 CV $$) dataset. The dataset was partially preprocessed with basic signal processing techniques such as bad channel repairing, independent component analysis for artifact removal, downsampling to 250 Hz, and an audio filter of 0.01–200 Hz for signal improvisation. This partially preprocessed dataset was further processed and was used in our deep learning model (DL) architectures. For EEG‐based biometric authentication, convolutional neural network (CNN) outperforms many of the state‐of‐the‐art DL architectures with a validation accuracy of approximately 99.86%, training accuracy of 98.49% and precision, recall and F1‐score with values of 99.91% that makes this EEG‐based approach for authentication more reliable. The DL models were also compared based on training and inference time, where CNN used the most training time but took the least time to predict the output. We compared the performance of the CNN model for three preprocessing techniques by feeding mel spectrograms, chromagrams and mel frequency cepstral coefficients, out of which mel spectrograms provided better results. This proposed architecture proves to be robust and efficient for military applications. [ABSTRACT FROM AUTHOR]

The impact of air quality inside residential buildings and constructions on the health of residents and workers has been studied very little in recent years. In the school environment, students are constantly exposed to mixtures of airborne substances from a wide variety of sources both in classrooms and in the school environment. Exposure to such factors of students influences, in the long term, physiological development, dynamics, quality of life, life expectancy, without being aware of the dangers in the air and being able to make decisions on reducing exposure to the risk factor. In the short term, poor air quality in classrooms leads to a decrease in attention and concentration, both for the student and the teaching staff. The research is motivated by the following: a) The lack of data and studies carried out in Romania, and their relationship with the change in student behavior during the school program; b) Laboratories and practice workshops in high schools are totally different from classrooms from the point of view of air quality and internal factors that contribute to pollution due to the equipment and specific, different activities carried out in these spaces. The novelty of this study is brought by the fact that, at the same time as the monitoring and data acquisition of air quality (inside and outside), there will be a monitoring and data acquisition of real-time biometric measurements of the subjects directly exposed to this environment, via a bracelet attached to the subject's arm. This bracelet is designed to perform biometric measurements without disturbing the subjects in the activities in which they are involved. These measurements are made with 9 different sensors for: pulse, blood oxygen (SPO2), air flow (respiration), body temperature, electrocardiogram (ECG), galvanic skin response (GSR - sweat), blood pressure (sphygmomanometer), patient position/movement, and muscle sensor/electromyography (EMG). The data thus obtained will be centralized on a PC and analyzed later. At the end, an ecological equipment capable of ensuring the ventilation of the space and a constant air quality, by monitoring the values of the compounds in the air, will be created experimentally. [ABSTRACT FROM AUTHOR]

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

Finger vein recognition (FVR) is a biometric trait that can authenticate the person in real-time applications. However, finger vein (FV) images are generally poor due to various unfavorable factors. Therefore, these images are prone to low contrast, insufficient brightness, and noise problems, significantly impacting the FVR system performance. Hence, image quality assessment and enhancement play a vital role in finger vein recognition. To advance the FVR system performance, we propose two FVR algorithms based on FV image quality estimation and an enhancement algorithm. At first, good quality feature such as contrast, entropy and information capacity were extracted from finger vein images. Then, the image quality is evaluated by the KNN with the r-smote technique to classify the FV image into two classes, High Quality (HQ) and Low Quality (LQ) images. Second, a novel enhancement method called guided filter and bilateral filter (GFBF) are presented to enhance the low-quality FV images. Afterward, we estimate the enhancement algorithm by using SSIM and PSNR. Finally, we evaluate and test the proposed system strength using two parameters—namely accuracy and equal error rate (EER), for Classifier and recognition performance, respectively, on a dataset of 1052 FV images. The completed experiment determined that the proposed image assessment and enhancement method outperformed other enhancement and assessment schemes by achieving a low identification error rate of 0.0335. Further results conclude that the proposed art would be a perfect pre-processing tool for finger vein feature-based algorithms. [ABSTRACT FROM AUTHOR]