Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Biometrics-Based Authentication: Advancements and Real-World Implementations

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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 2519

Share This Special Issue

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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

29 pages, 3925 KiB

Open AccessArticle

Beyond Signatures: Leveraging Sensor Fusion for Contextual Handwriting Recognition

by Alen Salkanovic, Diego Sušanj, Luka Batistić and Sandi Ljubic

Sensors 2025, 25(7), 2290; https://doi.org/10.3390/s25072290 - 4 Apr 2025

Viewed by 248

Abstract

This paper deals with biometric identification based on unique patterns and characteristics of an individual’s handwriting, focusing on the dynamic writing process on a touchscreen device. Related work in this domain indicates the dominance of specific research approaches. Namely, in most cases, only the signature is analyzed, verification methods are more prevalent than recognition methods, and the provided solutions are mainly based on using a particular device or specific sensor for collecting biometric data. In this context, our work aims to fill the identified research gap by introducing a new handwriting-based user recognition technique. The proposed approach implements the concept of sensor fusion and does not rely exclusively on signatures for recognition but also includes other forms of handwriting, such as short sentences, words, or individual letters. Additionally, two different ways of handwriting input, using a stylus and a finger, are introduced into the analysis. In order to collect data on the dynamics of handwriting and signing, a specially designed apparatus was used with various sensors integrated into common smart devices, along with additional external sensors and accessories. A total of 60 participants took part in a controlled experiment to form a handwriting biometrics dataset for further analysis. To classify participants’ handwriting, custom architecture CNN models were utilized for feature extraction and classification tasks. The obtained results showed that the proposed handwriting recognition system achieves accuracies of 0.982, 0.927, 0.884, and 0.661 for signatures, words, short sentences, and individual letters, respectively. We further investigated the main effects of the input modality and the train set’s size on the system’s accuracy. Finally, an ablation study was carried out to analyze the impact of individual sensors within the fusion-based setup. Full article

(This article belongs to the Special Issue Biometrics-Based Authentication: Advancements and Real-World Implementations)

► Show Figures

Figure 1

28 pages, 899 KiB

Open AccessArticle

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 255

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. 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)

► Show Figures

Figure 1

21 pages, 6937 KiB

Open AccessArticle

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 464

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 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)

► Show Figures

Figure 1

22 pages, 2411 KiB

Open AccessArticle

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 1089

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. 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)

► Show Figures

Journal Menu

Journal Browser

Biometrics-Based Authentication: Advancements and Real-World Implementations

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI