Biometric Authentication Systems: A Survey

Biometric authentication systems play a crucial role in ensuring secure access control in the era of the Internet of Things (IoT). Iris recognition, in particular, offers a highly accurate and reliable means of authentication. However, the success of iris recognition heavily relies on the accurate segmentation of the iris region from the surrounding areas. This research article presents a novel approach to iris segmentation using the Binary Bat Algorithm (BAT) in the context of biometric authentication in IoT. The proposed methodology involves preprocessing the iris images, defining an objective function to evaluate the quality of the iris segmentation, encoding the candidate solutions using binary encoding, initializing the population of bat solutions, and incorporating local search mechanisms within the BAT algorithm to fine-tune the segmentation. Experimental evaluations are conducted using a publicly available iris dataset, comparing the proposed BAT algorithm with existing segmentation methods. The performance of the BAT algorithm is assessed using evaluation metrics such as segmentation accuracy, completeness, and computational efficiency. Additionally, the robustness of the BAT algorithm is analyzed under various challenging conditions, including varying lighting conditions, occlusions, and noise. The results demonstrate that the BAT algorithm outperforms existing segmentation methods in terms of accuracy and completeness. The proposed approach shows promising potential for efficient iris segmentation in biometric authentication systems in the IoT domain.

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issues around privacy, spoofing, and database compromise because once biometric data leaks out, it cannot be withdrawn or substituted.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.

The biometric traits of any human being such as fingerprint, tongue prints, face recognition, Retina Scan, Iris recognition etc. are unique and cannot be replicated or fabricated using modern technologically advanced processes. These unique traits are used to identify individuals for grating access to sensitive data over the cloud. In this paper we propose a biometric authentication system based on Iris recognition to allow user identity based data to be pushed on to the cloud for global access, if the user Iris image is authenticated with the database. The real time Iris scanner captures the Iris image and same is imported to the MATLAB workspace for feature extraction and matching. Upon successful matching, the data is transmitted to the ThingSpeak Internet of Things (IoT) analytics platform. The IoT framework is a massive network of physical devices sharing data according to set protocols. Most of the IoT enabled devices lack security protocols. Thus, biometric authentication in general and iris recognition in particular finds a perfect fit for securing data over an IoT framework.

"This paper explores the application of neural networks in multimodal biometric authentication systems, emphasizing the integration of multiple biometric modalities to enhance security, accuracy, and robustness against various attacks. Traditional authentication methods, such as passwords and single-modal biometrics, often suffer from vulnerabilities, including spoofing, environmental factors, and data breaches. To address these challenges, multimodal authentication systems combine several biometric traits, such as facial recognition, fingerprint scanning, voice recognition, and keystroke dynamics, to achieve higher reliability and resistance to security threats. The study provides an overview of public datasets used for training neural network-based biometric authentication models, including VoxCeleb, RAVDESS, MOBIO, and SDUMLA-HMT. These datasets contain diverse biometric information necessary for developing robust multimodal authentication systems. The paper evaluates the effectiveness of existing approaches using key performance metrics such as accuracy, false acceptance rate (FAR), false rejection rate (FRR), and area under the curve (AUC). Additionally, specialized metrics are considered, including failure to enroll rate (FTE), failure to acquire rate (FTA), and template stability (TS), which are crucial for real-world applications. The role of neural networks in multimodal biometric authentication is analyzed by examining state-of-the-art architectures, including convolutional neural networks (CNNs) and deep learning-based feature fusion methods. Various fusion levels—feature-level, score-level, and decision-level—are discussed to determine the optimal integration strategy for improving authentication performance. The results indicate that multimodal systems significantly outperform unimodal authentication methods by reducing vulnerability to spoofing and environmental variations. Experimental findings suggest that integrating multiple biometric traits enhances the system’s adaptability to dynamic conditions, reducing both false acceptance and false rejection rates. Despite these advantages, several challenges remain, including computational complexity, data privacy concerns, and the need for real-time processing capabilities. Future research should focus on optimizing multimodal fusion techniques, improving generalization across different datasets, and enhancing the security of biometric templates against adversarial attacks. Additionally, developing lightweight neural network architectures suitable for mobile and embedded systems is essential for the practical deployment of multimodal authentication technologies"

Traditional identity verification of students based on the human proctoring approach can cause a scam identity verification and ineffective processing time, particularly among vast groups of students. Most student identification cards outdated personal information. Several biometric recognition approaches have been proposed to strengthen students’ identity verification. Most educational adoption technologies struggle with evaluation and validation techniques to ensure that biometric recognition systems are unsuitable for utilization and implementation for student identity verification. This study presents the internet of things to develop flexible biometric recognition systems and an approach to assess the quality of biometric systems for educational use by investigating the effectiveness of identity verification of various biometric recognition technologies compared to the traditional verification method. The unimodal, multimodal, and semi-multimodal biometric technologies were tested using the developed internet of things-base biometric recognition systems examined by applying the proposed quality metrics of scoring factors based on accuracy, error rate, processing time, and cost. Hundreds of undergraduate exam takers were a sample group. Key findings indicate that the designed and presented systems suitably attain identity verification of exam students using a unimodal biometric. The unimodal facial biometric system promises excellent support. A unimodal fingerprint biometric system ensures second excellent aid for student identity verification. However, multimodal and semi-multimodal biometric systems provide better accuracy with fewer handling times and higher costs. This study contributes significantly to the knowledge of utilizing biometric recognition for identity verification in smart educational applications.

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.

User authentication is a fundamental component of cybersecurity. Traditional password-based systems have long been the standard for verifying identity in digital environments, but increasing threats such as phishing, brute-force attacks, and password reuse have exposed their vulnerabilities. Biometric authentication using unique physiological or behavioural characteristics like fingerprints, facial recognition, or iris scans has emerged as a promising alternative. This paper presents a comparative study between password and biometric authentication systems focusing on their security, usability, reliability, and performance. A data-driven analysis using secondary data and user-based experiments highlights the differences in accuracy, authentication time, and user satisfaction. Results show that biometric systems outperform password authentication in terms of security and user convenience but face challenges related to privacy, cost, and environmental reliability. The paper concludes that hybrid models combining biometrics and passwords can balance usability and security for modern applications.

Biometrics based individual identification is observed as an effective technique for automatically knowing, with a high confidence a person's identity. Multi-modal biometric systems consolidate the evidence accessible by multiple biometric sources and normally better recognition performance associate to system based on a single biometric modality.Multi biometric systems are used to overcome this issue by providing multiple pieces of indication of the same identity. This system provides effective fusion structure that combines information provided by the multiple field experts based on decision-level and score-level fusion method, thereby increasing the efficiency which is not conceivable in uni-modal system.Multi-modal biometrics can be attained through a fusion of two or more images, where the subsequent fused image will be more protected. This paper discusses various fusion techniques, architecture of multi-modal biometric authentication and working of biometric fusion i.e. Iris and Fingerprint recognition that are used in multi-modal biometrics.

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.

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.

A biometric system is an amalgamation of algorithms that uses an individual’s unique biological data innate physical features (fingerprints, retina, face, ears, etc.) or behavioural features (signature, voice, keystroke dynamics, etc.) or both to authenticate the individual. This essentially makes a biometric system function as a pattern recognition system that helps an individual in any kind of adversarial environment. Hence, like any conventional authentication system, it is vulnerable to the attacks of malicious adversaries that try to manipulate the data and thus decline the security of the system by compromising its robustness. These possible attacks have not been considered while developing most biometric authentication systems. Our work describes multimodal biometrics, its feature extraction methods, research challenges and focuses on the major security issues in both unimodal and multimodal biometric systems. This paper summarizes some robust multimodal systems, their countermeasures and algorithms that have been devised to tackle these security challenges.KeywordsBiometricsMultimodal biometricsBiometric fusionSecurityAttacksCountermeasuresEncryption

In the modern digital world, biometric systems are a significant part of personal identification and access control. The use of sensitive biometric data such as iris and facial features raises huge privacy concerns because of the possibilities of data breaches and misuse. Therefore, this project focuses on the enhancement of the security of multimodal biometric systems through encryption mechanisms. The idea is to generate a strong biometric key based on features produced from iris and face biometric systems through advanced machine learning techniques. The features are then employed to produce a strong biometric key capable of encrypting the secret value using the AES algorithm. AES is a widely adopted symmetric encryption algorithm that ensures high efficiency in its software implementation and seamless processing capabilities of image data. This provides not only the reinforcement of authentication but also maintains confidentiality of biometric characteristics and mitigates the risks of privacy attacks and unauthorized access. Thus, this multimodal biometric encryption improves reliability, robustness, and the resistance power of the system against potential attacks, making it a promising step forward in the realm of secure biometric authenticationsystems. Keywords- Biometric encryption,Multimodal biometrics, ,Privacy attacks,Advanced Encryption Standard (AES), Biometric key generation,Iris recognition,Face recognition,Machine learning,Bio-crypto systems, Data security.

This paper investigates the potential of gait-based authentication for securing virtual environments, specifically within the Metaverse. With the growing need for reliable and secure identity verification in virtual spaces, traditional authentication methods, such as passwords or PINs, have proven insufficient. In contrast, biometric authentication systems, including gait analysis, provide a more secure and user-friendly alternative by leveraging unique physiological and behavioral traits for identity verification. This research applies machine learning algorithms—Random Forest and Support Vector Machine (SVM)—to gait data for distinguishing between authentic users and imposters. The dataset consists of 1,000 simulated gait samples with 16 features, such as stride length, step frequency, joint angles, and ground reaction forces (GRF). After performing exploratory data analysis (EDA), including feature distribution visualization and correlation analysis, two models were trained on the data. The Random Forest model outperformed the SVM model, achieving an accuracy of 56% and a recall of 76%, indicating its effectiveness in identifying authentic users. Despite the promising results, both models showed only marginal improvement over random guessing, highlighting the need for further optimization. This study contributes to the growing body of research on gait-based biometric systems by demonstrating their potential as a viable method for identity verification in virtual environments. It also identifies the most important gait features, such as step frequency, cadence variability, and knee joint angle, that significantly contribute to the classification process. Future research should explore advanced deep learning techniques and the integration of multimodal biometric systems to enhance the performance and reliability of gait-based authentication.

Biometric systems have been using widely and these systems have improved substantially which are being used for persons Authentication and verification. This Biometrics is playing a key role in Personal, National and Global security. The main approach of this paper is to tell the importance of Biometric systems. Different types of biometric systems have been used these days. These biometric systems are iris recognition, palm vein biometrics, voice recognition, face recognition. The main threat to all these Biometric systems is Spoofing. Facial recognition biometric system is being widely used when compared to other biometric systems. Different types of facial spoofing include mask attack; photo attack & video attack and Different methods used to detect Face spoofing are discussed in this paper. These face spoofing detection techniques include the use of LBP, DMD, SVM, PReLU, IDA, LFHOG and CNN. The Other Techniques Include a Combination of LBP, CNN, and IDA. However, the results strongly prove that the Implementation of the above techniques will detect Face spoofing.

Access control is the process of protecting resources by allowing or rejecting access to particular resources. It is an integral part of physical security systems and protects resources from unauthorized use. Over the past decade, there has been a huge demand and need for more sophisticated and secure access technologies. Scientists are developing cutting edge technologies to fulfill the requirements for secure access control experiences. Access control systems, using the latest biometric technologies such as fingerprint, facial recognition, iris recognition, DNA, and signature recognition have offered a higher level of security than the conventional systems. Biometrics plays a vital role in the identification and authentication phase of access control. Every biometric system has weaknesses and benefits. The system success depends on selecting the proper type of biometric modality to use and understanding how it affects system performance, cost, and limitations. Fingerprint holds the first place according to its popularity but facial recognition and iris recognition continue to grow for its accuracy and are considered as the future of biometric recognition. Besides biometric technology, other future possibilities for secure access control are- mobile access control, cloud-based security, and multi-factor authentication. This paper presents access control components, functions, and types. The aim of the paper is to discuss and analyze the latest biometric access control to secure sensitive information. This paper also provides an overview of access control, discusses the latest biometric technology, and presents the future trend of access control.