Generalized Net Model of Biometric Identification Process

Background. In recent years, the development of an automated identification process using biometric authentication has been observed, which has a high level of protection, since it allows you to evaluate the physical parameters and characteristics of a particular person. Such access control is more reliable since identifiers cannot be transferred to third parties or duplicated to bypass security systems. Over the past decades, a significant number of systems with biometric identification has been developed, but systems with identification according to the characteristics of the photoplethysmogram still receive little attention. The main task of biometric personality identification using photoplethysmogram is the search and implementation of machine learning methods to determine their belonging to a particular patient.Objective. The purpose of the paper is to develop an algorithm for distinguishing pulse wave iterations using the calculation of the temporal characteristics of photoplethysmograms, such as the maximum amplitude value, variance, mean absolute deviation, Wilson amplitude and the total sum of signal amplitude values.Methods. Based on the study of the temporal characteristics of the photoplethysmogram, an algorithm for distinguishing pulse wave iterations is created, which can be used for further biometric identification of a person using machine-learning methods.Results. The results can be used for further development of automated access control and management systems using biometric identification.Conclusions. Known methods of biometric identification are usually based on the static parameters of a person (the structure of the cornea of the eye, palm, fingerprints, geometry of the auricle, etc.), but have a low level of protection, since using special equipment you can create a copy of the biometric key. Therefore, today, the use of methods based on the parameters of dynamic biometric identification (plethysmogram, cardiogram and others) provides the highest degree of protection, but requires a more accurate software device to isolate and determine common symptoms. The proposed approach to calculating individual parameters of the photoplethysmogram with the aim of their subsequent classification by machine learning methods may be an acceptable solution for patient biometric identification systems.

Electrocardiographic (ECG) signals have been used for clinical purposes for a long time. Notwithstanding, they may also be used as the input for a biometric identification system. Several studies, as well as some prototypes, are already based on this principle. One of the methods already used for biometric identification relies on a measure of similarity based on the Kolmogorov Complexity, called the Normalized Relative Compression (NRC)—this approach evaluates the similarity between two ECG segments without the need to delineate the signal wave. This methodology is the basis of the present work. We have collected a dataset of ECG signals from twenty participants on two different sessions, making use of three different kits simultaneously—one of them using dry electrodes, placed on their fingers; the other two using wet sensors placed on their wrists and chests. The aim of this work was to study the influence of the ECG protocol collection, regarding the biometric identification system’s performance. Several variables in the data acquisition are not controllable, so some of them will be inspected to understand their influence in the system. Movement, data collection point, time interval between train and test datasets and ECG segment duration are examples of variables that may affect the system, and they are studied in this paper. Through this study, it was concluded that this biometric identification system needs at least 10 s of data to guarantee that the system learns the essential information. It was also observed that “off-the-person” data acquisition led to a better performance over time, when compared to “on-the-person” places.

In this work, we study the uncertainty aspects in bio-metric identification and authentication systems. The uncertainty lies in two aspects, i.e., the probability mass function (p.m.f.) of users' biometric information and the observation channel. For the p.m.f. of users' biometrics, it is assumed that the users are from different groups and the distribution of each group is determined by the state. For the observation channel, it is assumed that there is a set of channels and the actual observation channel is from this set.

This report provides an overview of Namibia’s biometric identification (ID) system. It centres on the national ID system that has evolved from paper based documentation dating from pre-1990 to increasingly biometric inclusive documentation that incorporates fingerprint and facial recognition technologies. The introduction of the of biometric ID, and its gradual rollout within the refugee community, specifically, is recognised as necessary to upscale service provision for essential services such as providing access to social grants, while also facilitating cross-border movement, and is among the many digital activities needed to transform Namibia into a knowledge-based economy. This research assesses the current state of the biometric ID system and existing legislative framework, while also comparing operational implementation against established rule of law, rights-based, and risk-based tests. This report evaluates the strengths and vulnerabilities in the system’s design and governance; it examines the system’s effectiveness, and balances security considerations with citizens’ rights to privacy, inclusion, and non-discrimination.

There is a pressing need for baseline demographic and health-related data to plan, implement and evaluate health interventions in developing countries, and to monitor progress towards international development goals. However, mobile pastoralists, i.e. people who depend on a livestock production system and follow their herds as they move, remain marginalized from rural development plans and interventions. The fact that mobile people are hard to reach and stay in contact with is a plausible reason why they are underrepresented in national censuses and/or alternative sequential sample survey systems. We present a proof-of-concept of monitoring highly mobile, pastoral people by recording demographic and health-related data from 933 women and 2020 children and establishing a biometric identification system (BIS) based on the registration and identification of digital fingerprints. Although only 22 women, representing 2.4% of the total registered women, were encountered twice in the four survey rounds, the approach implemented is shown to be feasible. The BIS described here is linked to a geographical information system to facilitate the creation of the first health and demographic surveillance system in a mobile, pastoralist setting. Our ultimate goal is to implement and monitor interventions with the "one health" concept, thus integrating and improving human, animal and ecosystem health.

Empowering robust biometric authentication: The fusion of deep learning and security image analysis

The paper explores the challenge of human identification through gait recognition within biometric identification systems. Itoutlines the essential criteria for human biometric features, discusses primary biometric characteristics, and their application in biometric identification systems. The paper also examines the feasibility of utilizing gait as a biometric identifier, emphasizing its advantages, such as not requiring the upfront provision of personal biometric information and specialized equipment.The authors conduct an analysis of existing scientific literature in the field of gait recognition, categorizing gait recognition methodsinto template-based and non-template-based approaches. Throughout their research, they identify the key issues and challenges that researchers face in this domain, along with the prevailing trends in human gait recognition within biometric identification systems.Additionally, the paper introduces a method for person identification based on gait, utilizing the Histogram of Oriented Gradients and the Sum Variance Haralick texture features. It involves transforming input video into a series of images depicting the gait silhouette, creating a Gait Energy Image (GEI) by combining these gait silhouettes throughout a gait cycle, and translating the GEI into the Gait Gradient Magnitude Image (GGMI). The subsequent step involves extracting recommended gait characteristics from the GGMIs of participants included in a dataset.To preprocess the collected characteristics, Principal Component Analysis (PCA) is applied, reducing the dimensions that may negatively impact classification robustness, thereby enhancing overall performance. In the final step, a K-Nearest Neighbors (KNN) classifier is employed to categorize the characteristics obtained from a specific dataset.The proposed novel feature vector in the paper demonstrates increased reliability and effectively captures spatial variations in gait patterns. Notably, it reduces the dimensionality of the feature vector from 3780×1 to 63×1, resulting in decreased computational complexity in the gait recognition system. Experimental evaluations on the CASIA A and CASIA B datasets reveal that the proposed approach outperforms other HOG-based methods in most scenarios, with the exception of situations involving frontal images.

The EU’s initiative to adopt the AI Act in August 2024 underscores the pressing necessity to regulate the use of AI-powered remote biometric identification (RBI) systems in public spaces for law enforcement purposes, striving to guarantee public security against terrorism and serious crime. The AI Act draws a critical distinction between real-time and ex-post biometric identification systems. While it prohibits practices posing significant risks to fundamental individual rights—including predictive policing, social scoring, and the surreptitious influencing of human behaviour—it does not impose absolute bans on some of the most rights-infringing uses of biometric technologies in publicly accessible spaces. This paper advocates for a nuanced balance between security imperatives and the protection of EU citizens’ fundamental rights and liberties. Judicial oversight serves to ensure transparency in the deployment of RBI systems, thereby guaranteeing a higher tier of protection against potential abuses of AI-driven technologies. Critics contend that the regulation's complexity may stifle Europe’s competitiveness in innovation and impair growth in the AI sector due to overregulation. However, the core democratic values of the European Union, alongside the fundamental principles of necessity, proportionality, and respect for individual rights, must serve as the compass for enforcing the AI Act in public spaces, without negating the need for Europe’s advancement in the AI sector.

One classification problem that is especially challenging is biometric identification, which links cybersecurity to the analysis of human behavior. Biometric data can be collected through the use of wearable devices, especially smartphones that incorporate a variety of sensors, during the performance of activities by the users. In recent research, numerous identification systems using machine learning classification algorithms have been proposed to provide solutions to this classification problem. However, their ability to perform identification is limited to only suitable selected features of time-series data from the biometric raw data. Therefore, in this study, an architecture for the biometric user identification using walking patterns that employs a convolutional neural network is proposed. Validation of the proposed framework for biometric identification was accomplished through the generation of synthetic data from datasets of samples from public walking. As a result, the framework described in this study provides more effective outcomes in terms of the accuracy of the model and additional metrics than the conventional machine learning utilized for biometric user identification.

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. An approach for accurate Biometric Recognition and Identification of Human Iris Patterns using Neural Network has been illustrated by gopikrishnan et. al. It has been concluded by Yingzi Du et. al that the partial iris portion of the iris pattern describes the uniqueness and the pupil has no direct effect on the accuracy of the biometric recognition. In this paper the Iris recognition has been carried out employing a template of size 10 × 480 pixels instead of 20 × 480 pixels as employed in the earlier paper. The results of the two sizes of the templates have been compared and it has been observed that the accuracy of the results obtained with the limited template size is comparable with that of the one with the full size. The reason for this is also discussed in this paper. The improved methodology suggested has resulted in the reduction of the space requirement as well as time complexity with no loss in accuracy. This paper also provides results of iris recognition performed applying Hamming distance, Feed forward back propagation, Cascade forward back propagation, Elman forward back propagation and perceptron. It has been established that the method suggested applying perceptron provides the best accuracy in respect of iris recognition with no major additional computational complexity.

Biometric identification provides a reliable performance on recognition of identity for different security and authentication management problems. With the development of technology, the privacy and security is the main concern as technology can be falsified. In recent decades biometric traits are the trusted choice to the researchers and there are strong documentations on biometric traits like human electrocardiogram which is the time changing electrical impulse signal passing through heart is able to use for identity subject identification. In this work, an analytic and serviceable analysis of the ECG for human identification is presented. A new approach based on chaotic encryption (CE) of continuous wavelet transformed (CWT) ECG signal has been proposed. The CWT used here to obtain similarity pattern of each individual and CE of the CWT decomposed signal used for subject identification. The proposed approach has been tested using 11 different subjects ECG signal available at MIT-BIH arrhythmia database. The obtained results show that the identification rate is 96.82%. The findings of this study can be helpful to develop effective biometric human identification system.

A novel compression-based 2D-chaotic sine map for enhancing privacy and security of biometric identification systems

Analysis of recent studies and publications. The analysis of the modern market of software and algorithmic solutions for performing the Liveness detection process showed that the currently existing approaches are completely commercial solutions with closed algorithms of their work. In addition, the Liveness detection algorithm is not yet standardized, and has many implementation options that can sometimes lead to am-biguous results [7]. That is why, it was decided to develop our own algorithm and liveness detection system based on obtaining face characteristics using the AWS API [8], because this service offers a high accuracy of face recognition, which is 99.99%, and provides 10,000 free requests to use every month, which enough to fulfill the purpose of our work. Purpose of the study. Development of the algorithm for the Liveness Detection process using AWS and the construction of a video identification system based on it. Main research material. This paper investigates the video identification of a per-son using the Liveness Detection process. The basis of the question, complexity of the Liveness Detection process and the implementation of biometric human video identifica-tion have been studied. An algorithm for conducting the Liveness Detection process based on the execution of random tasks by an identified person has been developed. Integration with the Amazon DetectFaces API was carried out in order to obtain the character of a digital image of the head, as a result of which it becomes possible to analyze a photo of a person for a wide range of key facial features. A manual test of the Amazon DetectFaces API was conducted, in the process of which, empirically, thresh-olds of facial characteristics in the image for the Liveness Detection process were set. Integration with the Amazon CompareFaces API was implemented to execute the bio-metric video identification. The testing of the developed Liveness Detection application system based on the created Liveness passing algorithm and selected AWS API thresholds consisted of 100 tests of different orientations, from direct user-flow testing to tests with a medical mask, glasses and beard, a photo of a face and a video image of a person for the purpose of forgery his personality. Thanks to the initial accuracy of Amazon API facial recognition of 99.99% and the random selection of tasks in the developed Liveness Detection algo-rithm, all tests gave the expected result - the person was either successfully video-identified at the enterprise, or his identity was faked during the Liveness Detection pro-cess. Conclusions. Based on the research, the software system of biometric human video identification has been developed, which allows to automatically establish the presence of a person in front of a video camera and identify it, preventing the use of fake artifacts to falsify a person's identity.

The paper demonstrates a need to study the functional reliability of identification systems and the applicability of methods for ensuring the functional reliability of information systems for biometric identification systems. Methods of ensuring functional reliability of biometric identification systems via providing indicators of reliability of identification devices and introducing redundancy are considered. The applicability of the methods for biometric identification systems is investigated, its advantages and disadvantages are evaluated. Triple modular redundancy scheme has been developed for the program redundancy using N-version programming. An appropriate reliability scheme based on the principles of dual programming has been developed to implement the identification system with one additional redundant element. Based on the NIST statistics of face recognition algorithms for the developed reliability schemes the reliability indicators were evaluated and its advantages over a system consisting of a single non-redundant element have been shown. The proposed reliability schemes are consistent with the practice of ensuring the functional reliability of information systems and can be integrated into biometric identification systems that use biometric data as the main access identifier.

Rapider responses are required from ATM banking suppliers to security requests. The current network environment has made it easier for hackers to engage in illicit activity. One of the most pressing issues confronting modern IT teams is network security. There is no doubt that ATM financial transactions should be protected against security risks by several different layers, but service providers should also incorporate security implications in their service offerings. And a lot concerning security flaws, credit card or identity card fraud, and methods for thieves and crackers to get their hands on any logical pin numbers or passwords. Identification, used in the current architecture to permit a system's access, is analogous to a username. Since passwords can be hijacked or misplaced, it is crucial to utilize authentication to confirm that the intended user is truly who their claim to be. The most recent solutions to security and privacy issues are biometric identification and authentication systems. Face recognition is the study of characteristics of a person being that can be used to recognize them. These physical characteristics include, among other things, fingerprints, faces, hand shapes, voices. This study surveys the various security system authentication methods. Top-level safety in authentication is provided by this facial biometric with QR code verification.