A biometrical data quality analysis method to reliably evaluate the efficiency of recognition algorithms and systems

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.

A primary consideration of this paper is to determine different factors influencing the reliability of performance evaluations of remote person recognition algorithms and systems. The authors suggest a method for determining and computing quantitative quality criteria of multimodal biometric data and consider the possibility of extrapolating test results to various practical applications. The functions of biometric data quality and biometric data artificiality that are introduced as a measure of proximity of the available biometric data to biometric data registered "naturally," i.e., data of unaware and noncollaborative subjects, are under examination in this paper.

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

Template-Protection-Techniken stellen eine wichtige Ergänzung zu biometrischen Systemen, um das gewünschte Sicherheitsniveau zu erreichen und die Anforderungen an den Schutz der Privatsphäre zu erfüllen. Diese Techniken ermöglichen Erneuerung und Widerruf der Referenzendaten in biometrischen Systemen. In dieser Arbeit wird ein systematisches Evaluierungsframework entwickelt, das sich an die wesentlichen Kriterien und Anforderungen an biometrische Template-Protection-Verfahren festhält. Seine Anwendbarkeit wird durch die Analyse der verschiedenen Algorithmen demonstriert. Die in dieser Arbeit präsentierte Evaluierung ist grundlegend für eine vollständige Analyse. Darüber hinaus ermöglicht es einen Nachweis der Sicherheit und Einhaltung der Privatsphäre. Es schafft eine Basis für Zertifizierung und Benchmark der Template-Protection-Verfahren.

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.

The article examines the implementation of artificial intelligence (AI) technologies for processing biometric data in Ukraine’s criminal proceedings. Emphasis is placed on the potential of AI use for identifying individuals by fingerprints, facial features, voice, and other biometric characteristics, which can enhance the efficiency of investigations and evidence gathering. At the same time, a number of risks are outlined: possible algorithm errors and false matches that could lead to unjustified suspicions or arrests; the issue of reliability and transparency of AI “black box” models, whereby parties in a trial cannot verify how a result was obtained; and privacy threats associated with the mass collection and use of sensitive personal (biometric) data without sufficient legal safeguards. It is determined that current Ukrainian legislation lacks clear provisions on the admissibility of evidence obtained through AI, in particular results of automated facial recognition or other biometric identifications. The need for improving the regulatory framework is substantiated: defining the procedural status of data generated by AI; introducing criteria for verifying their accuracy (through expert assessment, validation of methods, etc.); as well as establishing special procedures for judicial oversight over the use of such technologies at the pre-trial investigation and trial stages. Examples of foreign experience are provided: in some cases U.S. courts have admitted AI-driven analyses (e.g., DNA expert reports) as evidence, whereas other rulings have excluded evidence obtained via facial recognition technologies due to concerns about their reliability and the lack of disclosure of the AI methods. At the European level, a cautious approach is observed: the European Parliament advocates significant restrictions on the use of mass biometric surveillance systems, allowing them only in exceptional situations subject to a court order. It is highlighted that the introduction of AI into criminal justice practice must be accompanied by not only technical, but also legal and ethical safeguards. The conclusion is drawn that a comprehensive approach is required to balance the innovative capabilities of AI with human rights protections – from amending the Criminal Procedure Code to record and evaluate AI-derived evidence to taking into account international standards (GDPR, the EU AI Act) and creating mechanisms of independent oversight. Proper judicial control and tailored legal regulation will minimize risks and ensure the admissibility and objectivity of using AI-processed biometric data in criminal proceedings.

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.

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.

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.

A linear convolution-based cancelable fingerprint biometric authentication system

This paper discusses a system to determine the identification request for a given person already exists. The check is performed by comparing Applicant attributes (except for signature) against the registration database. For biometric information searches, a Biometric Identification System (BIS), which is an Automated Fingerprint Identification System (AFIS) Database, is searched against the captured biometric data on the application record. If a matched record already exists or a number of partially matched records exist, then application processing is routed to the “Resolve Duplicate” process. The Fraud Investigation application is built on Privacy Identification and Management Solutions (PRIMS) framework which is based on Identification and Credentialing solutions. The solutions are structured to capture biographic and biometric data in real-time with immediate checks on a person's identity, and produce e-ID cards and passports using this framework. The PRIMS framework supports identity management such as personnel enrolment and application processing, identity verification, secure document production and card inventory control, issuance and post-issuance management, authentication/authorization at point-of-service. The PRIMS framework is built with a flexible, component-based, service-oriented architecture. The PRIMS framework integrates a number of functions required to manage end-to-end life cycle of registered persons and secure documents. PRIMS provides a highly configurable workflow and business rules engine and allows for significant variability to support unique client requirements in the areas of enrolment processing, different technologies for secure documents, and different solutions and uses of biometrics.

Traditional biometric systems are designed and configured to operate in predefined circumstances to address the needs of a particular application. The performance of such biometrics systems tend to decrease because when they encounter varying conditions as they are unable to adapt to such variations. Many real-life scenarios require identification systems to recognise uncooperative people in uncontrolled environments. Therefore, there is a real need to design biometric systems that are aware of their context and be able to adapt to changing conditions. The context-awareness and adaptation of a biometric system are based on a set of factors that include: the application (e.g. healthcare system, border control, unlock smart devices), environment (e.g. quiet/noisy, indoor/outdoor), desired and pre-defined requirements (e.g. speed, usability, reliability, accuracy, robustness to high/low quality samples), user of the system (e.g. cooperative or non-cooperative), the chosen modality (e.g. face, speech, gesture signature), and used techniques (e.g. pre-processing to normalise and clean biometrics data, feature extraction and classification). These factors are linked and might affect each other, hence the system has to work adaptively to meet its overall aim based to its operational context. The aim of this research is to develop a multiagent based framework to represent a context-aware adaptive biometric system. This is to improve the decision making process at each processing step of traditional biometric identification systems. Agents will be used to provide the system with intelligence, adaptation, flexibility, automation, and reliability during the identification process. The framework will accommodate at least five agents, one for each of the five main processing steps of a typical biometric system (i.e. data capture, pre-processing, feature extraction, classification and decision). Each agent can contribute differently towards its designated goal to achieve the best possible solution by selecting/ applying the best technique. For example, an agent can be used to assess the quality of the input biometric sample to ensure the important features can be extracted and processed in further steps. Another agent can be used to pre-process the biometric sample if necessary. A third agent is used to select the appropriate set of features followed by another to select a suitable classifier that works well in a given condition.

Interest in detecting the psychophysical state of a person according to various biometric data (KGR, pupillography and profiling, etc.) is becoming more widespread every year. The most statistically reliable and resistant to counterfeiting systems are the identification of a person by the iris and face. Biometric identification systems can be combined. This paper presents proposals for improving the existing systems of recognition of the psychophysical state of a person.

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.

A cancelable biometric identification scheme based on bloom filter and format-preserving encryption