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Applied Sciences
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Applications of Signal Analysis in Biometrics
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Applications of Signal Analysis in Biometrics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 March 2025 | Viewed by 509

Special Issue Editors

Dr. Pengpeng Hu

E-Mail Website
Guest Editor
Research Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, UK
Interests: biometrics; measurement; point cloud processing; deep learning; 3D body scanning
Dr. Gengshen Wu

E-Mail Website
Guest Editor
Faculty of Data Science, City University of Macau, Macau, China
Interests: machine learning applications; computer vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vasile Palade

E-Mail Website
Guest Editor
Research Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, UK
Interests: artificial Intelligence; data science; machine learning; computational intelligence; neural networks; deep learning; neuro-fuzzy systems; various nature-inspired algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Biometrics is the science of recognizing individuals based on their physical, behavioral, and physiological traits, such as fingerprints, face, and iris, to name a few. Signal analysis has emerged as a foundational tool for enhancing the accuracy, security, and user convenience of biometric systems. As we witness the growing integration of biometric technologies across diverse sectors, including security, healthcare, and mobile applications, the role of sophisticated signal processing techniques has become increasingly pivotal. This Special Issue will explore the convergence of signal analysis with biometric technologies, aiming to address both existing challenges and new opportunities 

This Special Issue, titled “Applications of Signal Analysis in Biometrics”, will gather insights into cutting-edge research and innovations in signal analysis that enhance and expand the capabilities of biometric systems. We encourage submissions that not only advance the theoretical understanding of signal processing in biometrics but also demonstrate practical applications and innovations in real-world scenarios. By compiling these research efforts, we will foster a deeper understanding of how signal analysis can continue to revolutionize biometrics. 

We are particularly interested in submissions that cover, but are not limited to, the following topics:

  • Advanced algorithms for feature extraction and signal enhancement in biometric systems;
  • Application of machine learning and artificial intelligence in signal processing for biometric authentication;
  • Innovations in multimodal biometrics and their challenges in signal integration;
  • Security and privacy issues pertaining to the signal processing in biometric systems;
  • Performance evaluation metrics and standards for signal processing in biometrics;
  • Case studies demonstrating the implementation of signal analysis in various biometric applications, such as facial recognition, fingerprint analysis, and voice identification.

Dr. Pengpeng Hu
Dr. Gengshen Wu
Prof. Dr. Vasile Palade
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. Applied Sciences 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 2400 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

  • signal processing
  • biometric authentication
  • machine learning
  • security enhancements
  • feature extraction
  • multimodal biometrics
  • pattern recognition
  • privacy preservation
  • measurement
  • image processing

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

Published Papers (1 paper)

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Research

19 pages, 3001 KiB  
Article
Modular Neural Network Model for Biometric Authentication of Personnel in Critical Infrastructure Facilities Based on Facial Images
by Oleksandr Korchenko, Ihor Tereikovskyi, Ruslana Ziubina, Liudmyla Tereikovska, Oleksandr Korystin, Oleh Tereikovskyi and Volodymyr Karpinskyi
Appl. Sci. 2025, 15(5), 2553; https://doi.org/10.3390/app15052553 - 27 Feb 2025
Viewed by 142
Abstract
The widespread implementation of neural network tools for biometric authentication based on facial and iris images at critical infrastructure facilities has significantly increased the level of security. However, modern requirements dictate the need to modernize these tools to increase resistance to spoofing attacks, [...] Read more.
The widespread implementation of neural network tools for biometric authentication based on facial and iris images at critical infrastructure facilities has significantly increased the level of security. However, modern requirements dictate the need to modernize these tools to increase resistance to spoofing attacks, as well as to provide a base for assessing the compliance of the psycho-emotional state of personnel with job responsibilities, which is difficult to ensure using traditional monolithic neural network models. Therefore, this article is devoted to the development of a modular neural network model that provides effective biometric authentication for critical infrastructure personnel based on facial images, taking into account the listed requirements. When developing the model, an approach was used in which the functionality of each module was defined in such a way as to correspond to a task traditionally solved by a separate neural network model. This made it possible to use in each individual module a tested and accessible toolkit that has proven its effectiveness in solving the corresponding problem, which, in turn, compared to traditional approaches, allows for a 30–40% increase in the efficiency of the development and adaptation of authentication tools for the conditions of their application. Innovative features of the developed modular model include the ability to recognize spoofing attacks based on environmental artifacts and the naturalness of emotions, as well as an increase in the accuracy of person recognition due to the use of a U-Net neural network to highlight natural facial contours in occlusions. The experimental results show that the proposed model allows for a 5–10% decrease in person recognition error, recognition of spoofing attacks based on the naturalness of emotions and images of background objects, and recognition of the emotional state of personnel, which increases the efficiency of biometric authentication tools. Full article
(This article belongs to the Special Issue Applications of Signal Analysis in Biometrics)
►▼ Show Figures

Figure 1

Figure 1
<p>Sequential workflow of the modular neural network model.</p> Full article ">Figure 2
<p>Structures of the authors’ variants of neural network models designed for face recognition based on the comparison of two FIs. (<b>a</b>) Siamese Network, (<b>b</b>) Two_channel Network.</p> Full article ">Figure 3
<p>Drawing key points on a woman’s face. (<b>a</b>) Key points on a woman’s face, (<b>b</b>) key points on the face of a woman in transparent glasses.</p> Full article ">Figure 4
<p>Applying key points to an animated face. (<b>a</b>) No obstacles on faces, (<b>b</b>) obstacle—medical mask, (<b>c</b>) obstacle—dark glasses, (<b>d</b>) combined obstacle.</p> Full article ">Figure 5
<p>Variation in the accuracy indicator depending on the number of training epochs for the Siamese Network and Two_channel Network. (<b>a</b>) Siamese Network, (<b>b</b>) Two_channel Network.</p> Full article ">Figure 6
<p>Variation in the accuracy indicator depending on the number of training epochs for U-Net.</p> Full article ">Figure 7
<p>Selection of boundaries from an image of a male face in the form of a contour and in the form of a rectangular frame. (<b>a</b>) Face image, (<b>b</b>) segmentation of natural contours, (<b>c</b>) bounding box detection.</p> Full article ">Figure 8
<p>Selection of boundaries from an image of a female face in the form of a contour and in the form of a rectangular frame. (<b>a</b>) Face image, (<b>b</b>) segmentation of natural contours, (<b>c</b>) bounding box detection.</p> Full article ">
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