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Biometrics-Based Authentication: Advancements and Real-World Implementations
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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 1834

Special Issue Editors

Dr. Attaullah Buriro

E-Mail Website
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
Dr. Zahid Akhtar

E-Mail Website
Guest Editor
Department of Network and Computer Security, State University of New York Polytechnic Institute, C135, Kunsela Hall, Utica, NY 13502, USA
Interests: machine learning and computer vision with applications to cybersecurity; biometrics; deepfakes; affect recognition; image and video processing; perceptual-based audiovisual multimedia quality assessmentsing; perceptual-based audiovisual multimedia quality assessment; cybersecurity
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (3 papers)

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Research

28 pages, 899 KiB  
Article
Improving Presentation Attack Detection Classification Accuracy: Novel Approaches Incorporating Facial Expressions, Backdrops, and Data Augmentation
by Tayyaba Riaz, Adeel Anjum, Madiha Haider Syed and Semeen Rehman
Sensors 2025, 25(7), 2166; https://doi.org/10.3390/s25072166 - 28 Mar 2025
Viewed by 126
Abstract
In the evolving landscape of biometric authentication, the integrity of face recognition systems against sophisticated presentation attacks (PAD) is paramount. This study set out to elevate the detection capabilities of PAD systems by ingeniously integrating a teacher–student learning framework with cutting-edge PAD methodologies. [...] Read more.
In the evolving landscape of biometric authentication, the integrity of face recognition systems against sophisticated presentation attacks (PAD) is paramount. This study set out to elevate the detection capabilities of PAD systems by ingeniously integrating a teacher–student learning framework with cutting-edge PAD methodologies. Our approach is anchored in the realization that conventional PAD models, while effective to a degree, falter in the face of novel, unseen attack vectors and complex variations. As a solution, we suggest a novel architecture where a teacher network, trained on a comprehensive dataset embodying a broad spectrum of attacks and genuine instances, distills knowledge to a student network. The student network, specifically focusing on the nuanced detection of genuine samples in target domains, leverages minimalist yet representative attack data. This methodology is enriched by incorporating facial expressions, dynamic backgrounds, and adversarially generated attack simulations, aiming to mimic the sophisticated techniques attackers might employ. Through rigorous experimentation and validation on benchmark datasets, our results manifested a substantial leap in classification accuracy, particularly for those samples that have traditionally posed a challenge. The newly proposed model, which can not only effectively outperform existing PAD solutions, but also achieve admirable flexibility and applicability to novel attack scenarios, truly demonstrates the power of the proposed teacher–student framework. This paves the way for improved security and trustworthiness in the area of face recognition systems and the deployment of biometric technologies. Full article
(This article belongs to the Special Issue Biometrics-Based Authentication: Advancements and Real-World Implementations)
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21 pages, 6937 KiB  
Article
A Quantitative Analysis Study on the Effects of Moisture and Light Source on FTIR Fingerprint Image Quality
by Manjae Shin, Seungbong Lee, Seungbin Baek, Sunghoon Lee and Sungmin Kim
Sensors 2025, 25(4), 1276; https://doi.org/10.3390/s25041276 - 19 Feb 2025
Viewed by 342
Abstract
The frustrated total internal reflection (FTIR) optical fingerprint scanning method is widely used due to its cost-effectiveness. However, fingerprint image quality is highly dependent on fingertip surface conditions, with moisture generally considered a degrading factor. Interestingly, a prior study reported that excessive moisture [...] Read more.
The frustrated total internal reflection (FTIR) optical fingerprint scanning method is widely used due to its cost-effectiveness. However, fingerprint image quality is highly dependent on fingertip surface conditions, with moisture generally considered a degrading factor. Interestingly, a prior study reported that excessive moisture may improve image quality, though their findings were based on qualitative observations, necessitating further quantitative analysis. Additionally, since the FTIR method relies on optical principles, image quality is also influenced by the wavelength of the light source. In this study, we conducted a preliminary clinical experiment to quantitatively analyze the impact of moisture levels on fingertips (wet, dry, and control) and light wavelengths (red, green, and blue) on FTIR fingerprint image quality. A total of 20 male and female participants with no physical impairments were involved. The results suggest that FTIR fingerprint image quality may improve under wet conditions and when illuminated with green and blue light sources compared to dry conditions and red light. Statistical evidence supports this consistent trend. However, given the limited sample size, the statistical validity and generalizability of these findings should be interpreted with caution. These insights provide a basis for optimizing fingerprint imaging conditions, potentially enhancing the reliability and accuracy of automatic fingerprint identification systems (AFIS) by reducing variations in individual fingerprint quality. Full article
(This article belongs to the Special Issue Biometrics-Based Authentication: Advancements and Real-World Implementations)
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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 960
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
(This article belongs to the Special Issue Biometrics-Based Authentication: Advancements and Real-World Implementations)
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