Biometric Information Research Articles

The Association Between Physical Activity/Heart Rate Variability Data Obtained Using a Wearable Device and Timed Motor Functional Tests in Patients with Duchenne Muscular Dystrophy: A Pilot Study.

Duchenne muscular dystrophy (DMD) is a devastating X-linked muscle disease. Clinical evaluation of DMD uses patient-intensive motor function tests, and the recent development of wearable devices allows the collection of a variety of biometric information, including physical activity. In this study, we examined differences in physical activity and heart rate variability (HRV) between patients with DMD and healthy subjects using a wearable device, and investigated any association between these parameters and motor function in patients with DMD. Participants were 7 patients with DMD and 8 healthy males, whose physical activity and HRV were provided by a wearable device. These data were used to investigate the relationship between both physical activity and HRV parameters and timed motor functional tests [Time to stand from supine, 10-meter walking time (10MWT), North Star Ambulatory Assessment (NSAA), and 6-minute walking test (6MWT)] in patients with DMD. Results of 24-hours physical activity, fat burning, total number of steps and active distance, average step rate, average exercise intensity during walking, exercise, degree of forward lean during walking, maximum heart rate, normalized low frequency power (LF norm), and maximum exercise intensity in patients with DMD were lower than those in control subjects. Physical activity and HRV parameters did not correlate with the time to stand from supine. The 10MWT positively correlated with average heart rate, while NSAA negatively correlated with average heart rate, total frequency power (TF), and very low frequency power (VLF) during arousal. The 6MWT negatively correlated with ratio LF/high frequency power (HF). Physical activity and HRV indices that differ from those of normal children and that correlate with motor function assessment may serve as digital biomarkers.

Usability evaluation of a glove-type wearable device for efficient biometric collection during triage

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.

Класифициране на безсеменни сортове лози (Vitis vinifera L.) в зависимост от размерите на органите в женската сфера на цвета

A comparative evaluation and classification of a large group of seedless vine varieties was carried out depending on the diversity of the parameters of the female flower organs. Through the application of cluster analysis, it was found that in their vertical arrangement along the branches at the base of the fringe, they are distributed in four generalized clusters, in the middle - in six and in the top - in nine, each of them being composed of a different number of sub-clusters and varieties. There is no statistically proven influence of the location of the flower buttons in the fringe on the parameters of the female flower sphere. The presented biometric information on the female flower organs increases the possibilities for a more in-depth morphological, cytoembryological and ampelographic analysis and classification of the investigated seedless vine varieties.

Calculating Body Mass Index From 2D Image Using Convolutional Neural Networks and Anthropometric Measurements

Human body images encapsulate valuable biometric information. encompassing factors such as pupil color, gender, and weight. Of particular significance, body weight serves as a robust indicator of overall health. In alignment with recent health. science studies, this paper explores the viability of analyzing body weight through two- dimensional (2D) frontal-view human body images, utilizing the widely accepted Body Mass Index (BMI) as measure. To address varying levels of difficulty, three feasibility prob- lems are investigated, ranging from easy to hard. A comprehen sive framework is developed for body weight analysis, proposing the computation of five anthropometric features for precise body weight characterization. The correlation between the extracted anthropometric features and BMI values is thoroughly analyzed, affirming the usability of the selected features. For this study, a visual-body-to-BMI dataset is meticulously collected and cleaned, comprising 5900 images of 2950 subjects with corresponding labels for gender, height, and weight. findings demonstrate the feasibility of analyzing body The weight from 20 body images, with the proposed method outperforming two state-of-the-art facial image-based weight analysis approaches in most cases. Index Terms : Body weight analysis, visual analysis of body mass index (BMI), anthropometric features, visual-body-to- BMI dataset

The Processing of Forensic Investigative Leads in Criminal Investigations Conducted by the South African Police Service

The adverse impact of crime undermines not only a nation’s peace and stability, but also the safety and security of its communities. Furthermore, crime affects a nation’s economy, its citizens’ standard of living, and its standing with prospective foreign investors and tourists. Forensic investigative leads (FILs) are crucial in the investigation and resolution of crime. The South African Police Service (SAPS) allocates significant resources to analyse exhibit materials and forensic databases containing vital forensic biometric information. By conducting comparative searches in these databases, FILs are generated and communicated to detectives to facilitate investigation, detection and conviction. This article does an in-depth analysis of local and international literature, examines data from the CAS/ICDMS system and reports on qualitative interviews conducted with SAPS detectives, forensic examiners, and international forensic experts. It delineates the consequences of the underutilisation and an inadequate understanding of FILs on detection rates within the criminal justice system. The research not only identifies barriers to reporting and utilising FILs, but also recommends ways to enhance the regulatory framework, information technology systems, and detective training programmes within the SAPS. Emphasising adherence to ISO standards to maintain FIL reliability during forensic examinations, the study underscores the resource constraints faced by detectives, which affect FIL utilisation and impede the desired outcomes in detection and conviction rates. The article proposes that the SAPS can overcome these challenges by prioritising resource allocation to detective forensic investigative units to enable them to follow up and investigate FILs.

A Study on the Navigating Cybersecurity Challenges in the Digital-era

In today's era Navigating cybersecurity concerns is a top priority for individuals, corporations, and governments in today's digital landscape. This in-depth research explores the complex realm of cybersecurity, looking at the new dangers and the methods used to successfully counter them. The swift growth of digital technologies, encompassing cloud computing and the widespread use of Internet of Things devices, has greatly expanded the attack surface, revealing weaknesses that malevolent actors can promptly take advantage of. Gaining an understanding of these weaknesses is essential to creating strong defenses. The present study centers on pivotal components of cybersecurity strategy, commencing with the pivotal function of encryption in safeguarding confidential information. An essential layer of security is provided by encryption methods and protocols, which prevent unauthorized users from reading data. Furthermore, the application of multi-factor authentication is examined as a crucial security precaution. The security of digital assets is greatly increased by MFA, which requires users to submit several kinds of authentication, such as passwords, biometric information, or security tokens. The study also explores the significance of intrusion prevention systems and intrusion detection systems in preventing unwanted access. IDS keeps an eye on networks for unusual behavior or security policy violations and instantly notifies administrators of any hazards. people can learn how to spot and avoid social engineering scams. Organizations may greatly lower the chance of successful cyber intrusions by arming users with knowledge. Finally This report concludes by emphasizing how urgent it is to address cybersecurity issues in the digital age. A proactive, multi-layered protection plan is essential due to the increasing sophistication and pervasiveness of cyber threats.

A proof of concept: digital diary using 24-hour monitoring using wearable device for patients with Parkinson's disease in nursing homes.

In the advanced stages of Parkinson's disease (PD), motor complications such as wearing-off and dyskinesia are problematic and vary daily. These symptoms need to be monitored precisely to provide adequate care for patients with advanced PD. This study used wearable devices to explore biomarkers for motor complications by measuring multiple biomarkers in patients with PD residing in facilities and combining them with lifestyle and clinical assessments. Data on the pulse rate and activity index (metabolic equivalents) were collected from 12 patients over 30 days. The pulse rate and activity index during the off- and on-periods and dyskinesia were analyzed for two participants; the pulse rate and activity index did not show any particular trend in each participant; however, the pulse rate/activity index was significantly greater in the off-state compared to that in the dyskinesia and on-states, and this index in the dyskinesia state was significantly greater than that in the on-state in both participants. These results suggest the pulse rate and activity index combination would be a useful indicator of wearing-off and dyskinesia and that biometric information from wearable devices may function as a digital diary. Accumulating more cases and collecting additional data are necessary to verify our findings.

Elevating metaverse virtual reality experiences through network‐integrated neuro‐fuzzy emotion recognition and adaptive content generation algorithms

AbstractInteractions between individuals and digital material have completely changed with the advent of the Metaverse. Due to this, there is an immediate need to construct cutting‐edge technology that can recognize the emotions of users and continuously provide material that is relevant to their psychological states, improving their overall experience. An inventive method that combines natural language processing adaptive content generation algorithms and neuro‐fuzzy‐based support vector machines natural language processing (SVM‐NLP) is proposed by researchers to meet this demand. With this merging, the Metaverse will be able to offer highly tailored and engaging experiences. Initially, a neuro‐fuzzy algorithm was developed to identify people's emotional moods from their physiological reactions and other biometric information. Fuzzy Logic and Support Vector Machine work together to manage the inherent ambiguity and unpredictability, which results in a more exact and accurate categorization of emotions. A key component of the ACGA is NLP technology, which uses real‐time emotional data to dynamically modify and personalize characters, stories, and interactive features in the Metaverse. The novelty of the proposed approach lies in the innovative integration of neuro‐fuzzy‐based SVM‐NLP algorithms to accurately recognize and adapt to users' emotional states, enhancing the Metaverse experience across various applications. The proposed method is implemented using Python software. This adaptive approach significantly enhances users' immersion, emotional involvement, and overall satisfaction within the augmented reality environment by tailoring information to their responses. The findings show that the SVM‐NLP emotion identification algorithm based on neuro‐fuzzy, has a high degree of accuracy in recognizing emotional states, which holds promise for creating a Metaverse that is more emotionally compelling and immersive. Stronger human–computer interactions and a wider range of applications, including virtual therapy, educational resources, entertainment, and social media networking, might be made possible by integrating SVM‐NLP. These sophisticated systems are around 92% accurate in interpreting the emotions.

IEEE Transactions on Biometrics, Behavior, and Identity Science Information for Authors

IEEE Transactions on Biometrics, Behavior, and Identity Science Information for Authors

안면인식기술과 개인정보보호

Face recognition technology is a method that utilizes thermal infrared photography, three-dimensional measurement, and skeletal analysis to scan, store, and recognize facial shapes or thermal images. It verifies identity by comparing facial images captured by cameras with those in a stored photo database. This technology is extensively applied in various sectors, such as identity verification and access control, due to its high accuracy and quick identification capabilities. The advancement of facial recognition technology, coupled with CCTV, the Internet of Things (IoT), and widespread network connections, is materializing dystopian fears concerning omnipresent surveillance and the ensuing erosion of privacy. Additionally, biased facial recognition technology may serve as a tool for discrimination, infringing upon the rights and freedoms of minorities and other marginalized groups. In 2023, the National Human Rights Commission of The Republic of Korea issued a statement regarding human rights violations facilitated by facial recognition technology and urged the Speaker of the National Assembly and the Prime Minister to actively respond. The statement highlighted that direct observation of individuals' faces in public spaces by the state could limit citizens' freedom of movement and lead to unwarranted surveillance. Given the unique nature of biometric information, such as facial recognition technology, the repercussions of data breaches and misuse are more severe compared to other types of personal information. Specifically, real-time facial recognition technology that targets an unspecified number of individuals in public spaces without their consent may infringe upon the right to privacy and informational self-determination, as well as other fundamental rights including privacy, freedom of assembly, and freedom of association. Furthermore, real-time facial recognition security systems pose challenges in terms of privacy protection and invasion. Hence, the security of collected facial data, defenses against hacking threats, and the secure management of facial databases require thorough examination. This study explores the legal framework and international legislative trends related to biometric information, including facial recognition technology, to identify measures for protecting personal information and ensuring the safe use of this technology.

Biometric door lock system with automated fire detection alert

The reconciliation of biometric advancements and fire recognition components in shrewd homes is critical for access control and crisis readiness. The Blynk mobile app is used to access this project's biometric door lock and automated fire detection system. For access authorization, the biometric system uses cutting-edge fingerprint or facial recognition technology. The mechanized fire location framework screens the climate for potential fire dangers and triggers an alert when distinguished. The Blynk application gives controller and checking abilities, permitting inhabitants to lock or open the entryway, get ongoing warnings, and remotely screen the fire discovery framework's status. The microcontroller coordinates communications between the frameworks, outfitted with Wi-Fi availability. The Blynk application point of interaction is planned with easy-to-understand gadgets, permitting inhabitants to control and screen the coordinated frameworks. Safety efforts, for example, encoded correspondence conventions and secure stockpiling of biometric information, shield client protection. This all-encompassing approach to residential security ensures that their homes can effectively handle access control and emergencies, providing residents with security and peace of mind.

Pressure-Insensitive Epidermal Thickness of Fingertip Skin for Optical Image Encryption.

In this study, an internal fingerprint-guided epidermal thickness of fingertip skin is proposed for optical image encryption based on optical coherence tomography (OCT) combined with U-Net architecture of a convolutional neural network (CNN). The epidermal thickness of fingertip skin is calculated by the distance between the upper and lower boundaries of the epidermal layer in cross-sectional optical coherence tomography (OCT) images, which is segmented using CNN, and the internal fingerprint at the epidermis-dermis junction (DEJ) is extracted based on the maximum intensity projection (MIP) algorithm. The experimental results indicate that the internal fingerprint-guided epidermal thickness is insensitive to pressure due to normal correlation coefficients and the encryption process between epidermal thickness maps of fingertip skin under different pressures. In addition, the result of the numerical simulation demonstrates the feasibility and security of the encryption scheme by structural similarity index matrix (SSIM) analysis between the original image and the recovered image with the correct and error keys decryption, respectively. The robustness is analyzed based on the SSIM value in three aspects: different pressures, noise attacks, and data loss. Key randomness is valid by the gray histograms, and the average correlation coefficients of adjacent pixelated values in three directions and the average entropy were calculated. This study suggests that the epidermal thickness of fingertip skin could be seen as important biometric information for information encryption.

Social Implementation and Measurement Accuracy Verification of Non-contact Biological Monitors

In this research, for the purpose of social implementation, we conducted a near-miss demonstration experiment using a car driving in the city and a drive-simulator. Next, we conducted a demonstration experiment to evaluate the reliability of biometric information measured on patients in a medical facility's recuperation ward and residents of a special nursing home. NBC-1100 emits radar waves from a distance of up to 3 meters from an object and uses the reflected waves to measure biological information such as pulse waves and breathing waves. The multi-element pyroelectric effect was used to measure body temperature by detecting infrared radiation emitted from distant objects. This device is unique in that it can measure biological information without being restrained while wearing clothes. In the demonstration test, simultaneous measurements were conducted on nine healthy men aged 45 to 65 using a pharmaceutically approved product (μBP-mp) and a prototype non-contact biological monitor (NBC-1100 manufactured by K&S Co., Ltd.). The demonstration experiments at medical institutions and nursing care facilities were conducted with the consent of residents and their families and were conducted on 30 men and women between the ages of 70 and 94 who were undergoing treatment or in need of care. The tests were conducted on residents with chronic diseases such as dilated gastrostomies, symptomatic epilepsy, hypertension, Alzheimer's disease, and progressive supranuclear palsy. The evaluation method was simultaneous measurement using a master meter (μBP-mp) and a test meter (NBC-1100).

Effects of Sitting and Supine Positions on Tongue Color as Measured by Tongue Image Analyzing System and Its Relation to Biometric Information.

Tongue diagnosis is one of the important diagnostic methods in Kampo (traditional Japanese) medicine, in which the color and shape of the tongue are used to determine the patient's constitution and systemic symptoms. Tongue diagnosis is performed with the patient in the sitting or supine positions; however, the differences in tongue color in these two different positions have not been analyzed. We developed tongue image analyzing system (TIAS), which can quantify tongue color by capturing tongue images in the sitting and supine positions. We analyzed the effects on tongue color in two different body positions. Tongue color was quantified as L∗a∗b∗ from tongue images of 18 patients in two different body positions by taking images with TIAS. The CIEDE 2000 color difference equation (ΔE00) was used to assess the difference in tongue color in two different body positions. Correlations were also determined between ΔE00, physical characteristics, and laboratory test values. The mean and median ΔE00 for 18 patients were 2.85 and 2.34, respectively. Of these patients, 77.8% had a ΔE00 < 4.1. A weak positive correlation was obtained between ΔE00 and systolic blood pressure and fasting plasma glucose. Approximately 80% of patients' tongue color did not change between the sitting and supine positions. This indicates that the diagnostic results of tongue color are trustworthy even if medical professionals perform tongue diagnosis in two different body positions.

A secure fingerprint hiding technique based on DNA sequence and mathematical function.

DNA steganography is a technique for securely transmitting important data using DNA sequences. It involves encrypting and hiding messages within DNA sequences to prevent unauthorized access and decoding of sensitive information. Biometric systems, such as fingerprinting and iris scanning, are used for individual recognition. Since biometric information cannot be changed if compromised, it is essential to ensure its security. This research aims to develop a secure technique that combines steganography and cryptography to protect fingerprint images during communication while maintaining confidentiality. The technique converts fingerprint images into binary data, encrypts them, and embeds them into the DNA sequence. It utilizes the Feistel network encryption process, along with a mathematical function and an insertion technique for hiding the data. The proposed method offers a low probability of being cracked, a high number of hiding positions, and efficient execution times. Four randomly chosen keys are used for hiding and decoding, providing a large key space and enhanced key sensitivity. The technique undergoes evaluation using the NIST statistical test suite and is compared with other research papers. It demonstrates resilience against various attacks, including known-plaintext and chosen-plaintext attacks. To enhance security, random ambiguous bits are introduced at random locations in the fingerprint image, increasing noise. However, it is important to note that this technique is limited to hiding small images within DNA sequences and cannot handle video, audio, or large images.

E-Voting System: Applied Blockchain Concept for Implementation

One framework that lowers the proportion of abstention and guarantees security against vote tampering is the electronic voting system. Blockchain is a distributed, decentralized ledger that is used to efficiently and verifiably record transactions. Voter fraud is prevented by integrating Aadhar into the electronic voting system. With the use of voter’s VIDs (virtual IDs) that are gathered from the Aadhar database and biometric information, the proposed scheme offers a secure electronic voting method. The digital signature is used as the key to encrypt the votes within the block. Due to the present surge in interest in the use of blockchain technology for several applications, including identity, money, and healthcare, more attention has been paid to the legal ramifications of this technology than to its usefulness in administration. Thus, to eliminate the drawbacks of the current Indian voting system, and provide a better, more dependable, safe, and transparent form of public governance, we can create an application utilizing blockchain as an effective option for public voting. Blockchain technology is a means of governance, not only an object of regulation and governance. The powerful cryptography used by these technological elements offers a security level that is on par with or higher than any database that has ever been known

Audio-Visual Kinship Verification: A New Dataset and a Unified Adaptive Adversarial Multimodal Learning Approach.

Facial kinship verification refers to automatically determining whether two people have a kin relation from their faces. It has become a popular research topic due to potential practical applications. Over the past decade, many efforts have been devoted to improving the verification performance from human faces only while lacking other biometric information, for example, speaking voice. In this article, to interpret and benefit from multiple modalities, we propose for the first time to combine human faces and voices to verify kinship, which we refer it as the audio-visual kinship verification study. We first establish a comprehensive audio-visual kinship dataset that consists of familial talking facial videos under various scenarios, called TALKIN-Family. Based on the dataset, we present the extensive evaluation of kinship verification from faces and voices. In particular, we propose a deep-learning-based fusion method, called unified adaptive adversarial multimodal learning (UAAML). It consists of the adversarial network and the attention module on the basis of unified multimodal features. Experiments show that audio (voice) information is complementary to facial features and useful for the kinship verification problem. Furthermore, the proposed fusion method outperforms baseline methods. In addition, we also evaluate the human verification ability on a subset of TALKIN-Family. It indicates that humans have higher accuracy when they have access to both faces and voices. The machine-learning methods could effectively and efficiently outperform the human ability. Finally, we include the future work and research opportunities with the TALKIN-Family dataset.

Securing patient data in the healthcare industry: A blockchain-driven protocol with advanced encryption.

Ensuring the security and privacy of patient data is a critical concern in the healthcare industry. The growing utilization of electronic data transmission and storage in medical records has amplified apprehensions about data security. However, due to varying stakeholder interests, not all data can be freely shared, necessitating the development of secure protocols. This study presents a highly secure protocol that integrates blockchain technology, patient biometric information, and robust cryptographic algorithms (elliptic curve cryptography (ECC) and advanced encryption algorithm (AEC)) to facilitate data encryption and decryption. The protocol encompasses secure login, secure key sharing, and data sharing mechanisms among miners, offering comprehensive security measures. To validate the effectiveness of the proposed protocol, both informal and formal security analyses are conducted. The security protocol description language in Scyther is utilized to evaluate the protocol's resilience against attacks. The culmination of this research is a secure protocol that leverages blockchain technology and ECC for the secure storage and sharing of medical records. The protocol covers all stages, including system setup, user registration, login mechanisms, key exchange between users and blockchain, communication between blockchains, and interaction with other miners, with a steadfast emphasis on security. Furthermore, the protocol's communication and computation costs are assessed, with a comparison to existing blockchain-based schemes. Informal proofs establish the protocol's security against common attacks faced by medical institutions. Formal simulation of the protocol using the Scyther tool provides definitive evidence of its resistance to attacks. As a result, this protocol presents a viable real-time implementation solution for safeguarding patient data within the healthcare domain, representing a significant contribution to data security.

A Web Portal for Tracking and Finding Missing Person Using Aadhaar Enabled Biometrics

The Aadhaar-Enabled Biometric Web Portal for Finding Missing Persons research aims to address the critical issue of locating missing individuals by leveraging Aadhaar-enabled biometric data. With the widespread adoption of Aadhaar cards in India, this research uses the unique biometric information linked to Aadhaar cards to create a web portal. Through this portal, users can input relevant biometric data, such as fingerprints or iris scans, which are then compared with the Aadhaar database. The system utilizes advanced biometric matching algorithms to accurately identify potential matches, thereby aiding law enforcement agencies and concerned individuals in locating missing persons efficiently. By integrating Aadhaar-enabled biometrics into a user-friendly web interface, this research facilitates swift and accurate identification, offering a valuable tool in the effort to reunite missing individuals with their families and loved ones.

Iris-Based Biometric Identification Using a Combination of the Right - Left Iris Statistical Features

A combination between the information extracted for both right iris and left iris could increase the efficacy of the biometric recognition systems. In this paper, we propose a biometric identification method based on density of image patterns extracted from human iris images and the combination and comparison of the right iris and the left iris characteristics. The density of the patters approach for processed images can be a new biometric feature used to implement a biometric recognition system with high performance when a small feature dimension is used. In this way, we can maximize the retention of the effective biometric information. The experiments were conducted on the MMU Iris Database containing 225 images of the left eye and 225 images of the right eye. Two morphological Top-hat and Hit or Miss transforms were implemented to find out the particular pattern of pixels. They allow for the enhancement of detail in images. Then, a statistical feature extraction technique is employed to derive the density of the patterns in morphological transformed images. To assess the density of the patterns differences between the right and left iris data groups, the Pearson’s correlation coefficient (PCC) is computed. We reported very good results with a PCC of 0.6164 (strong and positive correlation) for Top-hat morphological operation whilst the Hit or Miss transform returns a PCC of 0.0127 so there is no relationship between the density of the patterns in the right and left irises.