Digital-ID in Namibia: Country Report

In the current paper a generalized net model of the biometric identification is presented. The modeled process contains combination of the steps included in unimodal, multibiometric and hybrid identification systems. The constructed generalized net model can be used for analyzing and simulating the processes included in biometric identification systems. It can be helpful in procedures of managing and optimizing the biometric identification processes. In the second part of the paper a MATLAB implementation of the biometric fingerprint identification is presented.

The National Identification System (NIS), an initiative of the government to create a computerized database of Ghanaians and foreign nationals resident in Ghana using a biometric system of registration to captures the biodata of an individual and stores it in a database. The NIS is expected to provide some benefits such as development planning based on sufficient accurate population data, delivery of social services such as health, retirement benefits and social administration; delivery of credit facilities; and identification of individuals for voting, insurance, licensing and general national security purposes. The NIS lacks a geospatial database and this could be a setback to the realization of the benefits of the NIS to the citizenry in Ghana. Literature reviewed shows that high quality geographic database is essential to having an effective citizenry identification system, to take care of the spatio-temporal dynamics which is key to the sustainability of such a system. Also this will aid policy makers, social scientist and government to geovisualise available citizenry data for research and decision making. This paper attempts to propose answers to the following questions: Can the NIS keep track of the spatial information pertaining to an individual or a group of people due to relocation? How can spatial information be validated during NIS registration? The paper first investigates novel geo-coding system that would be appropriate for providing spatial information of persons in Ghana. Secondly, the paper looks at the architectural framework for integrating a distributed geo-database into the NIS of Ghana. The paper then proposes a prototype system in the form of a Geo-demographic Information System for delivery of geographical data, demographic data, mapping capabilities and geo-information services. A system for validating location information within the NIS is discussed.

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.

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.

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.

Identification systems have become biometric-based, especially with the increase in the performance rates of machine learning methods. Biometric identification systems offer a high level of security by using reliable, difficult-to-change parameters. In this thesis, a biometric identification system is proposed using dorsal hand vein patterns. The relevant system has been tested on the sample dataset in the literature. The number of data were increased by adding noisy data to the data set used. The classification was made on the preprocessed images using SVM, ANN, LDA + KNN, and CNN methods. It has been determined that the highest identification accuracy is achieved when CNN is used, and CNN method provides higher performance compared to other methods. With the proposed identification system, after multiple runs, an average accuracy of 99.64% is achieved with the CNN machine learning method.

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.

The need for fool proof authentication procedures away from traditional authentication mechanisms like passwords, security PINS has led to the advent of biometric authentication in information systems. Biometric data extracted from physiological features of a person including but not limited to fingerprints, palm prints, face or retina for purpose of verification & identification is saved as biometric templates. The inception of biometrics in access control systems has not been without its own hitches & like other systems it has its fair share of challenges. Biometric fingerprints being the most mature of all biometric spheres are the most widely adopted biometric authentication systems. Biometric systems effectiveness lies on how secure they are at preventing inadvertent disclosure of biometric templates in an information system’s archive. This however has not been the case as biometric templates have been fraudulently accessed to gain unauthorized access in identification and verification systems. In order to achieve strong and secure biometric systems, biometric systems developers need to build biometric systems that properly secure biometric templates. Several biometric template protection schemes and approaches have been proposed and used to safeguard stored biometric templates. Despite there being various biometric template protection schemes and approaches in existence, none of them has provided the most authentic, reliable, efficient and deterrent means to totally secure biometric fingerprint templates. This research sought to establish status of the current biometric template protection techniques and methods by conducting a survey and analyzing data gathered from a sample of seventy-eight (78) respondents. We will report these results and give our conclusion based on findings of the survey in this paper.

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.

Electoral processes in the Democratic Republic of the Congo (DRC) have faced persistent challenges since the first democratic elections in 2006, particularly regarding transparency and trust. Despite the introduction of electronic voting machines in 2018 and 2023, concerns remain over electoral fraud, notably the lack of robust mechanisms to prevent multiple registrations and votes by the same individual. These vulnerabilities continue to undermine public confidence in the Independent National Electoral Commission (CENI). This study proposes a multimodal biometric authentication system to enhance the credibility and security of the electoral process. The proposed system integrates fingerprint and facial recognition technologies with RFID (Radio-Frequency Identification) card functionality, aiming to secure both voter registration and voting phases. To evaluate public perception, a survey was conducted among Congolese citizens to assess their experiences with past elections and their trust in CENI’s fraud prevention capabilities. Findings revealed widespread dissatisfaction and a strong interest in adopting biometric technologies. A prototype of the system was developed and tested with 25 graduate students at Hanyang University. Among the three authentication methods, fingerprint recognition yielded the highest user satisfaction (93.8%), followed by RFID (92.5%) and facial recognition (88.5%), indicating strong potential for improving usability and trust in future DRC elections.

Biometric identification systems like fingerprint, face, iris, and voice recognition have revolutionized user authentication with more convenience and increased security over conventional password based authentication. Nonetheless, the fact that biometric features are immutable introduces unprecedented issues around privacy, spoofing, and database compromise because once biometric data leaks out, it cannot be withdrawn or substituted. This work advances empirical testing with analytical analysis to test biometric authentication for face, fingerprint, and iris modalities. Models based on convolutional neural networks (CNNs) were used and evaluated on benchmarked datasets, and findings indicated that face and iris recognition performed nearly perfectly with zero false acceptance and rejection rates in controlled environments, while fingerprint recognition performed poorly because of dataset size and quality constraints. The conclusions identify the significance of data preparation and variation in ascertaining the reliability of biometric information. In an effort to cross-verify the experiments further, case studies of Aadhaar, Apple Face ID, and Biostar 2 also clarified the threats of central storage, spoofing, and regulatory loopholes. The research concludes by suggesting privacy preserving frameworks, encryption, and multimodal methods for securing the future of biometric authentication.

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

Biometric systems based solely on one-modal biometrics are often not able to meet the desired performance requirements for large user population applications, due to problems such as noisy data, intra-class variations, restricted degrees of freedom, nonuniversity, spoof attacks, and unacceptable error rates. Multimodal biometrics refers to the use of a combination of two or more biometric modalities in a single identification system. The most compelling reason to combine different modalities is to improve the recognition accuracy. This can be done when features of different biometrics are statistically independent. This paper overviews and discusses the various scenarios that are possible in multimodal biometric systems using fingerprint, face and iris recognition, the levels of fusion that are possible and the integration strategies that can be adopted to fuse information and improve overall system accuracy. This paper also discusses how the image quality of fingerprint, face and iris used in the multimodal biometric systems affects the overall identification accuracy and the need of staffing for the secondary human validation. For a large user population identification system, which often has more than tens or hundreds of millions of subject images already enrolled in the matcher databases and has to process more than hundreds of thousands of identification requests, the system's identification accuracy and the need of staffing levels to properly operate the system are two of the most important factors in determining whether a system is properly designed and integrated

This paper presents a novel biometric identification system with high performance based on the features obtained from human retinal images. This system is composed of three principal modules including blood vessel segmentation, feature generation, and feature matching. Blood vessel segmentation module has the role of extracting blood vessels pattern from retinal images. Feature generation module includes the following stages. First, the optical disk is found and a circular region of interest (ROI) around it is selected in the segmented image. Then, using a polar transformation, a rotation invariant template is created from each ROI. In the next stage, these templates are analyzed in three different scales using wavelet transform to separate vessels according to their diameter sizes. In the last stage, vessels position and orientation in each scale are used to define a feature vector for each subject in the database. For feature matching, we introduce a modified correlation measure to obtain a similarity index for each scale of the feature vector. Then, we compute the total value of the similarity index by summing scale-weighted similarity indices. Experimental results on a database, including 300 retinal images obtained from 60 subjects, demonstrated an average equal error rate equal to 1 percent for our identification system.

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.