Introduction and Preliminaries on Biometrics and Forensics Systems

Summary form only given. Biometrics refers to the automatic identification (or verification) of an individual (or a claimed identity) by using certain physical or behavioral traits associated with the person. By using biometrics it is possible to establish an identity based on `who you are¿, rather than by `what you possess¿ (e.g., an ID card) or `what you remember¿ (e.g., a password). Therefore, biometric systems use fingerprints, hand geometry, iris, retina, face, vasculature patterns, signature, gait, palmprint, or voiceprint to determine a person¿s identity. The purpose of this tutorial is two-fold: (a) to introduce the fundamentals of biometric technology from a pattern recognition and signal processing perspective by discussing some of the prominent techniques used in the field; and (b) to convey the recent advances made in this field especially in the context of security, privacy and forensics. To this end, the design of a biometric system will be discussed from the viewpoint of four commonly used biometric modalities - fingerprint, face, hand, and iris. Various algorithms that have been developed for processing these modalities will be presented. Methods to protect the biometric templates of enrolled users will also be outlined. In particular, the possibility of performing biometric matching in the cryptographic domain will be discussed. The tutorial will also introduce concepts in biometric fusion (i.e., multibiometrics) in which multiple sources of biometric information are consolidated. Finally, there will be a discussion on some of the challenges encountered by biometric systems when operating in a real-world environment and some of the methods used to address these challenges.

Biometric authentication has long been regarded as a foundational element of identity verification, leveraging unique physiological and behavioral traits to enhance security beyond traditional passwords. While it offers notable advantages such as convenience and resistance to identity theft, concerns are mounting regarding privacy, susceptibility to spoofing, and the irreversibility of compromised biometric identifiers. These weaknesses are becoming increasingly critical as digital infrastructures evolve into distributed, dynamic environments in which static trust models are no longer sufficient. Moreover, several traditional modalities- such as fingerprints, iris scans, and voice recognition- have already been breached. However, Artificial Intelligence (AI) methods are reshaping this landscape by introducing adaptive and context‑aware features into biometric systems. Machine Learning (ML) techniques enhance accuracy, enable continuous authentication, and support multimodal fusion, while anomaly‑detection mechanisms improve resilience against sophisticated attacks. Generative AI (GenAI) plays a particularly significant role, though it introduces a paradox: it empowers defenders through realistic attack simulations and robustness testing, yet simultaneously equips attackers with tools for producing deepfakes and synthetic identities, thereby expanding the attack surface. In this evolving security landscape, Zero‑Trust Architectures (ZTA) have gained prominence as a model that replaces assumptions of inherent trust with continuous verification mechanisms. The use of biometric data within ZTA can enhance the reliability of identity verification; however, it also intensifies several existing issues. Biometric identifiers must be handled and stored in ways that safeguard individual privacy and align with relevant legal requirements, and the incorporation of AI‑based assessment methods introduces additional concerns regarding potential bias, transparency, and oversight. Moreover, combining AI‑supported biometric systems with Zero‑Trust principles raises further questions about scalability, system compatibility, and the broader ethical consequences of more pervasive identity monitoring. This work therefore examines the convergence of biometrics, AI, and Zero‑Trust principles from a critical perspective. It highlights the dual role of AI as both a source of innovation and a generator of new threats, while identifying opportunities for adaptive security, real‑time threat detection, and improved user experience. By analyzing technical and operational dimensions, the work proposes a roadmap for integrating biometrics into ZTA that balances innovation with accountability and supports trustworthy, resilient cybersecurity frameworks.

Information and Communication Technologies are increasingly entering in all aspects of our life and in all sectors, opening a world of unprecedented scenarios where people interact with electronic devices embedded in environments that are sensitive and responsive to the presence of users. Indeed, since the first examples of “intelligent” buildings featuring computer aided security and fire safety systems, the request for more sophisticated services, provided according to each user’s specific needs has characterized the new tendencies within domotic research. The result of the evolution of the original concept of home automation is known as Ambient Intelligence (Aarts & Marzano, 2003), referring to an environment viewed as a “community” of smart objects powered by computational capability and high user-friendliness, capable of recognizing and responding to the presence of different individuals in a seamless, not-intrusive and often invisible way. As adaptivity here is the key for providing customized services, the role of person sensing and recognition become of fundamental importance. This scenario offers the opportunity to exploit the potential of face as a not intrusive biometric identifier to not just regulate access to the controlled environment but to adapt the provided services to the preferences of the recognized user. Biometric recognition (Maltoni et al., 2003) refers to the use of distinctive physiological (e.g., fingerprints, face, retina, iris) and behavioural (e.g., gait, signature) characteristics, called biometric identifiers, for automatically recognizing individuals. Because biometric identifiers cannot be easily misplaced, forged, or shared, they are considered more reliable for person recognition than traditional token or knowledge-based methods. Others typical objectives of biometric recognition are user convenience (e.g., service access without a Personal Identification Number), better security (e.g., difficult to forge access). All these reasons make biometrics very suited for Ambient Intelligence applications, and this is specially true for a biometric identifier such as face which is one of the most common methods of recognition that humans use in their visual interactions, and allows to recognize the user in a not intrusive way without any physical contact with the sensor. A generic biometric system could operate either in verification or identification modality, better known as one-to-one and one-to-many recognition (Perronnin & Dugelay, 2003). In the proposed Ambient Intelligence application we are interested in one-to-one recognition,

This research paper aims to contribute to the field of biometric attendance systems. Biometric system which uses unique biological traits like fingerprints for identification and authentication, offers several benefits over traditional authentication methods like passwords or ID cards. The benefits of using biometric system can be enhanced security, convenience and high efficiency, reduced risk of fraud, cost-effective in long run, accountability, scalability, multi-factor authentication, etc. In most of the organizations or institutes attendance is taken by the traditional methods like register book but this method takes time then attendance from these registers is uploaded to server for further attendance report of students. To withstand this problem, we have proposed an IoT based multipurpose biometric authentication system. We have developed a portable system that includes fingerprint sensors to recognize the fingerprint of various students and show the details of the students whose fingerprints have been scanned. The data from the fingerprint scanner is sent to the Raspberry PI server using Wi-Fi. We also have also designed a user interface for students and faculty to view and manage the students and their attendance. The attendance report can be exported to excel file any time faculty want the report. The biometric system provides 80% efficiency over the regular attendance methods.

A recognition technique is essential in practically every industry in the current digital era. It has several advantages and may be used for security, identification, and authentication. The relevance of access control systems based on biometrics has grown in recent years since they have the ability to address the majority of the shortcomings of existing security systems. Automated biometric systems for human identification take a measurement of the body's "signature," compare it to a database, and make an application-specific determination. These biometric methods for personal verification and identification are based on physiological or behavioral traits that are usually recognizable, despite changing over time, such as fingerprints, hand geometry, the face, voice, lip movement, gait, and iris patterns. The purpose of this study is to conduct a thorough literature review in order to pinpoint the most well-known recognition techniques, applications, and obstacles.

Many studies in the last decade have been dedicated to the use of the handwriting as a feature in the human identification, opening the door to consider it as a biometric identifier. In this paper we provide an evaluation of the handwriting as a potential biometric identifier according to a standard framework. Firstly, we present the required conditions for a human characteristic to be considered as a biometric identifier. We also provide a generic biometric system, detailing all its various components, and the two associated modes in which it can operate: Identification and Verification. In the second part of this paper we evaluate and discuss the validity of the handwriting as biometric identifier based on the framework we defined.

In recent years large number of emerging automated applications faces the need to have recognition abilities of persons using their own self biometrics, before they can access the applications services. Nowadays, Biometric recognition is used, it can be used as automatic identification or automatic verification of persons based on their physiological or behavioral characteristics. There are no perfect biometric measurements; each biometry has its advantages and limitations. Each biometry requires specific vital identity to answer the identification or verification question. The suitability of a particular biometry for a particular application depends on many factors. Hand geometry/shape is a very simple biometric technology that uses the measurements of human hand to verify the identity of the individuals. The measurements include the distance between certain mark points, shape and width of fingers and size of palm. The biometric systems that employing hand geometry become widely used since they have high public acceptance. This article aims to survey several articles found in literature about hand based biometric system, and to compare different methods of biometric recognition that based on hand geometry.

Individual recognition systems with high-speed and high-accuracy have been recently demanded in the automatic logging into a PC, the immigration at the airport, the access control and diligence & indolence management in an office, and so on. Biometric authentication is now being regarded as the most valid method because of the receptivity, individuality and invariability of biometric identifiers. Various types of the individual recognition systems based on biometrics have been studied and partially realized. Fingerprints, faces, hand geometry, irises, vein patterns, gait, signatures, etc., are known as biometric identifiers. In particular, the fingerprint recognition system has been widely used because of its high reliability and reasonable price (Maltoni et al., 2003a; Jain et al., 2010). The fingerprint recognition methods can be classified into the following three types: (i) the minutiae-based, (ii) the frequency-based and (iii) the image-based methods. The minutiae-based method is mainly being used in the practical fingerprint recognition system. For example, a memetic fingerprint matching algorithm has been recently proposed to identify the optimal or near optimal global matching between two minutiae sets (Sheng et al., 2007). A matching technique for fingerprint recognition using the Minutia CylinderCode (MCC), which is based on 3D data structures built from minutiae distances and angles, has also been made a proposal (Cappelli et al., 2010) to exclude the drawbacks in the fingerprint authentication using local minutiae structures. Moreover, a fingerprint verification using spectral minutiae representations has been suggested to overcome translation, rotation and scaling which are the drawbacks of minutiae-based algorithms (Xu et al., 2009a, 2009b). The frequency-based method, such as the frequency analysis method, is also being used in the practical fingerprint recognition system in order to secretly hide the original fingerprint information (Takeuchi et al., 2007). Recently, a fingerprint recognition method based on melfrequency cepstral coefficients and polynomial shape coefficients has been proposed because of the robustness to noise and the insensivity to translation (Hashad et al., 2010). The image-based method has been being studied to improve the accuracy in the fingerprint recognition. For example, an enhanced image-based algorithm for fingerprint verification based on invariant moment features has been recently proposed to improve matching accuracy and processing speed (Yang & Park, 2008a, 2008b). A novel image-based fingerprint matcher based on the minutiae alignment has also been made a proposal to improve the verification performance (Nanni & Lumini, 2009).

This review article's objective is to describe how one of the major advantages of biometric technology, which involves identifying and verifying people by examining their bodily characteristics, in employee attendance helps in better functioning of HR practices. The majority of biometric technology users struggle with the challenge of choosing an precise and cost-effective biometric system in addressing specific issues in a given environment, despite the many benefits of the biometric system and its influence on many job sectors throughout the world. To improve the conventional staff attendance system, which currently has an impact on the organization's efficiency, this article investigates the biometric attendance identifier that may be utilized. Implementing a qualitative (exploratory) technique, the study was conducted. It. is purely exploratory research that provides comprehensive details on biometrics, biometric attendance systems, and their application to Talent Management. Nevertheless, the study demonstrates that a biometric identifier is efficient and cost-effective for the organization's human resources attendance management system as an element of HR procedures, which implies that consideration is given before suggesting the use of biometric technology to improve the effectiveness of business operations in a firm.

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases.

As biometric systems are deployed in increasingly diverse applications, it becomes correspondingly important to understand the impact which human aging has on system performance. Aging directly affects those physiological and behavioral traits which are characterized in biometric measurements, and a practical biometric system must be designed to account for age-induced changes. However, age can also have very positive implications, for example as a source of further identification information. This paper reviews research to understand how age factors impinge on biometric systems and uses this to synthesize a system infrastructure to unify implementation principles. We present new results to show how multiagent structures can provide an effective framework for this purpose, enhancing performance in both identification and predictive scenarios.

The emerging of The Law 23 of 2006 on Population Administration is a step to control the issuance of population and the development of population database. Residents are allowed to have one ID card only. The issuance of a single ID card and the establishment of an accurate and a complete population database require technological support to ensure the accuracy of a person’s identity and the identity card which has a strong authentication method and a high identity data security to prevent counterfeiting and duplication. The use of biometric systems in electronic Id card (e-KTP) makes it possible to the owner of the e-KTP to be connected into a single national database. Therefore each resident requires one ID card only. The objective of this research is to explore the extent of the effectiveness and the efficiency of the use of biometric systems on the preparation of national database based on single-KTP number (NIK) in Bandung. The method of this research is qualitative descriptive analysis technique. The Measuring of the effectiveness and the efficiency is the dimensions of system quality Wilkinson. The effectiveness of the use of biometric systems in the process of e-KTP in Bandung is effectively categorized based on quality systems indicators such as: Relevancy, Capacity, Timeliness, Accessibility, Flexibility, Accuracy, Reliability, Security, Economy and Simplicity. The Efficiency of the use of the biometric system is quite efficient because the reviewing of the recording results can be done more quickly and automatically, biometric systems can increase productivity and efficiency as well as reduce job turnaround time in checking the accuracy of the data with the card owner.

Assessment of personality types in Nigerian dwarf goats (Capra hircus) and cross-context correlations to behavioural and physiological responses

Biometric authentication systems offer a secure and convenient method of verifying the identity of individuals based on their unique physiological or behavioral traits. This paper presents a comprehensive survey of biometric authentication systems, focusing on the principles, technologies, applications, challenges, and future directions in the field. The survey covers various biometric modalities, including fingerprint recognition, iris recognition, facial recognition, keystroke dynamics, voice recognition, and gait analysis. Key topics discussed include the use of multi-modal biometric systems, continuous authentication, machine learning, and AI in biometrics, and biometric encryption. The paper also explores the implementation of biometric authentication in access control systems, financial transactions, healthcare systems, and government and law enforcement. Challenges such as security and privacy concerns, spoofing and impersonation, scalability and integration, and ethical and legal issues are addressed. Future research opportunities include improving accuracy and reliability, enhancing security and privacy, addressing societal and ethical concerns, and integrating biometric authentication with the Internet of Things (IoT).

Due to peg-free system for image acquisition, it is obvious to have a motion of user’s hand. Motion activity creates the blurring effect in captured images, which leads to the false identification of a person. In such cases, image deblurring is required to restore the image. This paper proposes hand geometry- and palm texture-based person identification system with motion blur deblurring of palm images. The deblurring process may generate the ringing artifacts. The quality of the restoration of a motion blurred image is depending on the estimation of motion blur parameters, blur angle and blur length. In this paper, Radon transform and cepstral method have been used for the blur parameter estimation. Point spread function (PSF) can be constructed using blur parameters. For the deblurring of the palm images, L2/TV regularization based on augmented Lagrangian method has been used. It deblurs the image with the removal of ringing artifacts. Restored images are further used in the biometric system to analyze the robustness of the system against the motion blur. This system is tested on a database collected from 100 people. 99.5% genuine acceptance rate (GAR) is achieved for bimodal system against motion blur for discrete cosine transform (DCT)-based features. Equal error rate (EER) achieved is 1.1719.