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Biometrics-Based Authentication: Advancements and Real-World Implementations

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 730

Special Issue Editors


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Guest Editor
Faculty of Engineering, Free University of Bozen-Bolzano, 39100 Bozen-Bolzano, Italy
Interests: biometrics (physical/behavioral); authentication and access control using human behaviors; machine learning; data mining; generative adversarial networks
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Network and Computer Security, State University of New York Polytechnic Institute, Utica, NY 13502, USA
Interests: computer vision; machine learning and pattern recognition with applications to biometrics; cybersecurity; affect recognition; image and video processing; perceptual-based audio-visual multimedia quality assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In an era where digital security is of paramount importance, biometric authentication stands at the forefront of technological advancements, offering a robust alternative to traditional security measures. This Special Issue, “Biometrics-Based Authentication: Advancements and Real-World Implementations,” delves into the cutting-edge developments and real-world applications of biometric technologies. From fingerprint scanning to facial recognition, these methods are rapidly becoming integral components of security systems across various sectors. Biometric sensors play a crucial role in capturing unique individual traits, thus aligning perfectly with the scope of Sensors. This Issue aims to explore the innovative sensor technologies that enable biometric systems to provide reliable and efficient user authentication.

This Special Issue seeks contributions that address the latest sensor technologies in biometrics, their integration into current security frameworks, and the challenges and opportunities they present in the context of real-world implementation. The focus is on how these sensors detect and process unique identifiers, ensuring secure and seamless access control.

Dr. Attaullah Buriro
Dr. Zahid Akhtar
Guest Editors

Manuscript Submission Information

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Keywords

  • biometric sensors
  • fingerprint recognition
  • facial recognition
  • iris scanning
  • voice authentication
  • security systems
  • behavioral biometric systems
  • authentication algorithms
  • sensor technology
  • access control
  • identity verification

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Published Papers (1 paper)

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Research

22 pages, 2411 KiB  
Article
A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain–Computer Interfaces to Enhance Motor Imagery Classification
by Souheyl Mallat, Emna Hkiri, Abdullah M. Albarrak and Borhen Louhichi
Sensors 2025, 25(2), 443; https://doi.org/10.3390/s25020443 - 13 Jan 2025
Viewed by 506
Abstract
Enhancing motor disability assessment and its imagery classification is a significant concern in contemporary medical practice, necessitating reliable solutions to improve patient outcomes. One promising avenue is the use of brain–computer interfaces (BCIs), which establish a direct communication pathway between users and machines. [...] Read more.
Enhancing motor disability assessment and its imagery classification is a significant concern in contemporary medical practice, necessitating reliable solutions to improve patient outcomes. One promising avenue is the use of brain–computer interfaces (BCIs), which establish a direct communication pathway between users and machines. This technology holds the potential to revolutionize human–machine interaction, especially for individuals diagnosed with motor disabilities. Despite this promise, extracting reliable control signals from noisy brain data remains a critical challenge. In this paper, we introduce a novel approach leveraging the collaborative synergy of five convolutional neural network (CNN) models to improve the classification accuracy of motor imagery tasks, which are essential components of BCI systems. Our method demonstrates exceptional performance, achieving an accuracy of 79.44% on the BCI Competition IV 2a dataset, surpassing existing state-of-the-art techniques in using multiple CNN models. This advancement offers significant promise for enhancing the efficacy and versatility of BCIs in a wide range of real-world applications, from assistive technologies to neurorehabilitation, thereby providing robust solutions for individuals with motor disabilities. Full article
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