Optimizing biometric system selection via complex spherical fuzzy einstein aggregation operators.

Interpretable evaluation of physiological signals for biometric identification.

Finger vein recognition has emerged as a highly robust and intrinsically stable biometric technology, demonstrating great potential in identity authentication and intelligent security applications. However, conventional methods still suffer from constraints in feature representation and computational efficiency, particularly under challenging conditions such as illumination variation, pose deviation, and noise interference. To address these challenges, this study presents an efficient finger vein recognition approach based on a lightweight convolutional neural network (LCNN) architecture. The proposed framework integrates a multi-stage image preprocessing pipeline for automatic vein region detection, advanced denoising, and refined texture enhancement, which is subsequently followed by compact feature modeling within a lightweight deep network. Extensive experiments on the public Shandong University Machine Learning and Applications-Homologous Multi-Modal Traits (SDUMLA-HMT) dataset and a self-acquired Laboratory Finger-Vein (Lab-Vein) dataset validate the superiority of the proposed method, achieving recognition accuracies of 97.1% and 98.3%, respectively, surpassing existing benchmark models. Moreover, the model demonstrates notable reductions in parameter complexity and computational cost, achieving an average inference time of only 12.6 ms, which confirms its strong real-time capability and suitability for embedded deployment. Overall, the proposed approach attains a desirable trade-off between accuracy and efficiency, offering meaningful implications for the advancement of lightweight biometric recognition systems.

Attendance management is an essential administrative task in educational institutions and organizations, yet traditional methods such as manual roll calls and register-based systems are time- consuming, error-prone, and vulnerable to proxy attendance. With the rapid advancement of Artificial Intelligence (AI) and computer vision technologies, automated biometric systems have emerged as efficient alternatives. This research presents the design, development, and implementation of an AI- Based Face Recognition Attendance System integrated with a real-time web dashboard for monitoring and management. The proposed system utilizes computer vision techniques for face detection and deep learning-based facial encoding for recognition. Real-time video frames captured through a webcam are processed to detect faces, extract distinctive facial features, and compare them with pre-stored facial encodings in a database. Upon successful recognition, attendance is automatically recorded along with date and timestamp information, eliminating manual intervention. The system is developed using Python, OpenCV, and a Flask-based web framework, with structured database integration for secure and organized data storage. In addition to automated attendance marking, the system incorporates a dynamic administrative dashboard that provides real-time statistics, attendance summaries, and historical trend visualization. The dashboard enhances usability by allowing administrators to manage student records, monitor attendance performance, and generate reports efficiently. Experimental evaluation demonstrates a recognition accuracy ranging from 92% to 96% under standard indoor lighting conditions, with processing time under two seconds per individual. The proposed solution reduces administrative workload, prevents proxy attendance, ensures contactless operation, and improves record accuracy. Although minor limitations exist under low-light conditions or partial facial occlusion, the system demonstrates strong reliability and scalability for practical deployment. This research highlights the effectiveness of integrating AI-driven facial recognition with web-based management systems to modernize attendance processes in educational and organizational environments..

Existing biometric systems are predominantly built upon 2D biometrics which are vulnerable to presentation attacks, and have a limited coverage of the biometric surface. 3D biometrics is emerging due to the rapid development of 3D sensing technology. 3D biometrics is effective in defending against spoofing attacks and potentially offer more robust performance under different conditions. However, there exist many challenges in developing effective 3D biometric systems in terms of 3D biometrics reconstruction and recognition. In this paper, we present a systematic survey on the latest developments of 3D biometric systems guided by our proposed taxonomy of methods for 3D biometrics.

From Unimodal to Flexible: A Survey of Generalized Biometric Systems

Biometric authentication is now central to modern identity and access management, offering stronger security and a better user experience than traditional passwords. From 2025 to 2026, advances in artificial intelligence, sensor technology, and decentralized identity models have broadened the adoption of biometric systems in consumer, business, and government sectors. However, emerging threats such as deepfakes, adversarial machine learning, and large-scale data breaches have exposed significant vulnerabilities and ethical issues. This paper reviews recent trends and shortcomings in biometric authentication, comparing major methods, including fingerprint, facial, iris, voice, and behavioral recognition. It assesses their performance, reliability, usability, scalability, and security, and examines the increasing adoption of multimodal, continuous, and privacy-preserving authentication systems. The analysis addresses key challenges, including spoofing, bias, template theft, and accessibility, to identify current system limitations. The paper proposes a layered mitigation framework that integrates technical safeguards, system design enhancements, governance measures, and user-focused strategies. It contends that the effectiveness of biometric authentication relies on both technological advancement and ethical, transparent practices that foster public trust. Addressing technical and social factors is essential to developing secure, fair, and widely accepted biometric systems.

The ongoing evolution of cybersecurity has necessitated a paradigm shift toward zero-trust architectures, in which no single component, user, or device is inherently trusted. In this context, the emerging technology of on-device security (such as iOS Secure Enclave based on biometric-sealed keys) can be regarded as a novel security system wherein cryptographic functions are hard-bound to user-specific biometric data stored within a secure, non-exportable enclave. This review discusses the technical foundation of biometric-sealed keys, privacy-preserving biometric systems, and their integration with federated identity and post-quantum cryptography. The importance of mutable biometrics, the generation of dynamic keys utilizing fuzzy logic, and low-latency encryption in relation to multi-hop authentication are further examined. Despite the considerable advantages, fallback vulnerabilities, device interoperability issues, and ethical concerns must also be considered in efforts to fully harness the potential of this technology. Privacy-first security architectures will be redefined through biometric sealing and secure execution environments that are resilient to hardware-level attacks and aligned with zero-trust principles in the modern digital ecosystem.

The rapid growth of Extended Reality (XR) Internet services has raised significant security concerns, especially for immersive visual identity authentication. Deepfake-based impersonation attacks can harm user trust and data confidentiality. Traditional biometric systems, which are mainly unimodal facial recognition, are susceptible to synthetic media manipulation and adversarial spoofing. This paper proposes an immersive visual identity authentication system, coupled with a deepfake detection system that leverages multimodal feature fusion to enhance security in XR settings. The given model integrates spatial-temporal facial representations, periocular texture representations, voice spectral representations, and behavioural motion patterns via a hybrid attention-based fusion network. An evaluation was conducted on a dataset of 18,500 authentic and 17,300 deepfake XR interaction samples. Experiments show that the multimodal fusion model achieves an authentication accuracy of 98.7%, which is much higher than that of unimodal models (face-only: 92.4%; voice-only: 89.1%). The proposed deepfake detection module achieves the following precision, recall, F1-score, and false acceptance rate (FAR): 97.9%, 98.3%, 98.1%, and 1.2%, respectively, representing a 43% decrease in spoofing vulnerability compared to traditional CNN-based detectors. Additionally, real-time viability is verified through latency analysis, with an average authentication cycle processing delay of 34 ms, which is within the constraints of immersive XR services. The results suggest that multimodal feature fusion is associated with a high level of resistance to identity verification in immersive Internet ecosystems under synthetic identity manipulation. The proposed framework will contribute to a secure, scalable, and reliable authentication infrastructure for next-generation XR-enabled digital services.

Explainable Deep Liveness Detection: Balancing Transparency, Security, and Compliance in Workforce and Government Biometric Systems

Facial recognition and soft biometrics systems often face difficulties in the presence of facial hair, particularly beards, which significantly modify the structure and texture of the face. Existing facial datasets rarely include beard-specific annotations, limiting the robustness of models in real-world conditions. To address this limitation, we introduce BFSET (BeardFaceSet), a curated dataset comprising 4800 fully annotated facial images featuring various beard styles, densities, and poses. Each image includes precise bounding box coordinates of the beard region, enabling targeted analysis and learning. A statistical evaluation based on entropy, GLCM, and HOG features confirms the visual diversity and texture complexity of the dataset. In addition, benchmarking experiments using BFSET demonstrated improved beard detection accuracy (mAP ≈ 0.93) and recognition performance when integrated with existing datasets. BFSET is therefore a valuable resource for the development and evaluation of models that consider beards in facial recognition and soft biometrics.

Digital technologies have moderately reduced inflated voter accreditation figures in Nigeria; however, other forms of electoral fraud continue to undermine electoral integrity. This study argues that technology-based elections have repeatedly suffered biometric device failures because critical enabling factors—such as electricity, internet connectivity, and technical expertise—remain inadequate, hindering the smooth implementation of biometric policies. These challenges have hybridised the electoral system, as policy reversals and the adoption of manual accreditation have undermined INEC’s commitment to fully govern voter accreditation through biometric devices. The study draws on the cybernetic communication model and the frustration–aggression hypothesis for theoretical insights, and employs the documentary method for data collection, with descriptive statistics and content analysis used for data analysis. Findings indicate that biometric device failures, largely caused by infrastructural and human resource deficiencies, facilitate fraud, exacerbate electoral conflicts, and render contests highly controversial. Such failures open avenues for electoral manipulation, generating discontent that manifests in lethal violence, rejection of outcomes through litigation, and voter apathy. These results underscore the urgent need for policy reforms aimed at strengthening technological adoption and improving enabling infrastructure and human capacity, thereby maximizing the intended benefits of biometric systems in Nigerian elections.

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.

Given ADNOC’s strategic push toward digital transformation, it is critical to understand how ICT adoption translates into tangible improvements in manufacturing performance. To address this, the study tested the proposed framework using 356 valid responses from ADNOC employees, analysed through SmartPLS. The model demonstrated strong fit and validity, confirming significant direct effects of biometric systems (0.155), drone technology (0.161), face detection and tracking (0.19), and two-way radio communication (0.18) on manufacturing performance, while robotics showed no direct significance. Training emerged as a critical mediator, fully mediating the effect of robotics and partially mediating the effects of drones, two-way radios, and biometric systems. No mediation was observed for face detection and tracking. These findings highlight the key role of workforce training in unlocking the full potential of ICT technologies, thereby enhancing manufacturing performance in ADNOC’s operations.

Biometric ticketing systems utilizing fingerprint recognition provide enhanced security and convenience for passenger identification in public transportation. However, the transmission of fingerprint templates over wireless networks without adequate cryptographic protection exposes the system to interception attacks and privacy breaches. This research implements AES-128 encryption in Cipher Block Chaining (CBC) mode to protect fingerprint templates transmitted within an ESP32-based biometric ticketing system. The implementation leverages the ESP32’s integrated mbedTLS library with hardware acceleration to achieve efficient cryptographic operations. Experimental evaluation using 10 fingerprint template samples demonstrates a 100% success rate for encryption-decryption operations. Performance measurements indicate an average encryption latency of 2.30 ms and decryption latency of 2.10 ms, with a data size overhead of 32 bytes (6.25%) due to Initialization Vector (IV) and PKCS7 padding. The results confirm that the proposed encryption scheme effectively secures biometric data transmission while maintaining system responsiveness suitable for real-time applications.

ABSTRACT The increasing use of AI-driven surveillance technologies at West African borders marks a shift in ECOWAS’s approach to regional security, migration management, and crime control. Biometric identification systems, predictive analytics, and automated profiling, often influenced by European migration externalisation policies, are becoming central to border governance. This article critically examines the human rights implications of these technologies, focusing on their effects on migrants′ rights, data protection, and the ECOWAS principle of free movement. Through legal and policy analysis, it assesses the compatibility of AI-driven border surveillance with international and regional human rights frameworks, including the African Charter on Human and Peoples’ Rights. The article argues that while AI may enhance border security, it also deepens risks of digital authoritarianism, racialised profiling, and exclusionary migration policies. Drawing on case studies from Nigeria and Niger, it calls for stronger regional safeguards, oversight mechanisms, and human rights centred AI governance in West Africa and contributes to ongoing debates on cyber sovereignty, migration, and AI ethics in the region.

The iris recognition system is one of the most reliable biometric authentication systems owing to its individuality and permanence. With the introduction of deep learning concepts, especially CNNs, iris recognition models have improved tremendously in accuracy, robustness, and efficiency. We present an extensive survey summ2arizing the application of deep learning in iris recognition; covering datasets, feature extraction, architectures, and evaluation metrics. We conclude that while CNNs and transfer learning produce the best accuracy on well-constrained datasets, serious challenges lie with cross-sensor generalization and robustness under mobile or unconstrained environments. The new solutions that are generating attention, such as generation adversarial network-based augmentation and attention-driven architectures, are solution pathways to surmount data scarcity and further strengthen the adaptation capability. This survey is aimed at steering researchers and practitioners to various critical challenges and directions that have the most potential for future iris recognition systems.

The article analyzes the impact of varying the bit depth (quantization) of a neural network’s output tensor on speaker recognition accuracy. This tensor represents the neural network’s latent space, containing the latent features utilized for speaker recognition tasks. Typically, 32 bits are allocated per value in the output space (the output tensors of the methods under study contain 512 values), resulting in significant memory requirements for maintaining a continuously updated database. Consequently, the "minifloat" floating-point format is of particular interest, as it enables numerical representations using only 8, 6, or 4 bits. To ensure comprehensive results, three neural network models demonstrating superior recognition performance on the test set were selected: CAM++, WavLM, and ReDimNet. These models possess unique architectural characteristics, facilitating the assessment of how bit depth reduction affects recognition accuracy across different neural network architectures. Recognition accuracy is evaluated using the Equal Error Rate (EER). The evaluation employs the English-language VoxCeleb-1 dataset, the audio characteristics of which correspond to those of a small-scale biometric system database. The relevance of this study is underscored by the increasing volume of research proposing the use of voice as a verification key. Therefore, managing large biometric datasets requires substantial storage capacity and RAM. Modern databases are continuously updated and expanded, leading to increased resource demands for their maintenance. Applying quantization to the neural network’s output tensor offers a potential solution. However, excessive reduction of the bit depth in the output tensor can lead to a significant degradation in recognition quality compared to the baseline network. The primary focus of this research is to minimize the resources required to support a biometric system without the need for additional neural network training.

Background/Objectives: Safe delivery and correct identification of newborns are critical aspects of healthcare systems globally. The accreditation of healthcare and standards regulation significantly promotes the adoption of modern technologies to address risks related to infant abduction and misidentification. The effectiveness and extent of these mandates vary across settings and countries. Therefore, this study aims to map and explore modern technologies used for safe newborn delivery and correct identification aligned with healthcare accreditation and regulatory frameworks. Methods: This review adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis extension for scoping reviews (PRISMA-ScR) guidelines. The Problem, Intervention, Comparison, and Outcome (PICO) framework was employed to facilitate the development of the research question. This study examined studies reporting technologies such as radio frequency identification (RFID), biometric identification, and real-time monitoring across healthcare settings for infant protection through the Normalization Process Theory (NPT). Among three databases and search engines (PubMed, Google Scholar, and Web of Science). The risk of bias for each study was assessed using the AACODS Checklist, SQUIRE 2.0 Checklist, TIDieR Checklist, and JBI tools. Results: Out of 8753 records, only 27 reports were eligible to be included in this review. The most frequently reported technologies were RFID systems (11 studies, 37.9%) and biometric systems such as footprint and facial recognition (6 studies, 20.7%). Despite strong technological potential, many healthcare institutions struggled with the adoption of infant protection technologies. Accreditation systems among the high-resource settings actively mandate advanced technologies and support the integration of staff training and simulation drills. Comparably, middle- and low-income regions usually face challenges related to regulatory enforcement, infrastructure, staff readiness, and limited adoption of modern technologies. Conclusions: Accreditation and standards development are critical catalysts for the adoption of modern infant protection technology. Standards must be comprehensible, adaptable, and supported by investment in human resources and infrastructure. Future regulation must focus on strengthening enforcement, continuous quality improvement, and capacity building to achieve sustainable protection across the world.

Signing EEG-based biometric authentication system using multivariate Fourier-Bessel series expansion-based entropies