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Trustless Biometric Sensors and Systems
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Trustless Biometric Sensors and Systems

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

Deadline for manuscript submissions: 20 November 2024 | Viewed by 2066

Special Issue Editors

Prof. Dr. Kristijan Lenac

E-Mail Website
Guest Editor
Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia
Interests: mobile robotics; satellite navigation; embedded systems; blockchain
Dr. Žiga Emeršič

E-Mail Website
Guest Editor
Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: biometry; object detection; deep learning

Special Issue Information

Dear Colleagues,

Biometrics and blockchain are two disruptive technologies that promise to facilitate more streamlined and secure authentication and identity management in the digital world. Blockchain brings several important benefits to biometric systems, including data integrity, accountability, availability, and universal access. The secure and private storage or verification of biometric templates in the blockchain and/or delegation of feature extraction and matching processes will make it even more difficult to circumvent biometric authentication. On the other hand, biometrics can streamline and secure blockchain identities; enable new governance models, smart devices, and wallets; and propose new use cases where security and usability are fully balanced.

The Special Issue will explore the potential of blockchain for trustless biometric systems, examine how the two technologies can benefit from each other, and discuss opportunities and challenges with the goal of advancing understanding of the synergies between the two fields. Topics include, but are not limited to, the following: smart biometric sensors and systems, privacy-preserving biometric methods, decentralised matching methods, delegating computations, the protection of biometric templates and privacy, and smart contracts for the trust management of biometric data.

Prof. Dr. Kristijan Lenac
Dr. Žiga Emeršič
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

  • smart biometric sensors
  • decentralized biometric sensors
  • trust management in blockchain
  • trust management in biometrics
  • biometric data privacy
  • trusted biometric systems
  • distributed processing of biometric data
  • digital identity
  • decentralised biometrics
  • decentralised identity
  • biometric authentication
  • zero knowledge authentication
  • privacy-preserving biometry
  • biodata security
  • biometric trust
  • biometric wallets

Published Papers (1 paper)

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Research

22 pages, 17811 KiB  
Article
Probabilistic Fingermark Quality Assessment with Quality Region Localisation
by Tim Oblak, Rudolf Haraksim, Laurent Beslay and Peter Peer
Sensors 2023, 23(8), 4006; https://doi.org/10.3390/s23084006 - 15 Apr 2023
Cited by 1 | Viewed by 1589
Abstract
The assessment of fingermark (latent fingerprint) quality is an intrinsic part of a forensic investigation. The fingermark quality indicates the value and utility of the trace evidence recovered from the crime scene in the course of a forensic investigation; it determines how the [...] Read more.
The assessment of fingermark (latent fingerprint) quality is an intrinsic part of a forensic investigation. The fingermark quality indicates the value and utility of the trace evidence recovered from the crime scene in the course of a forensic investigation; it determines how the evidence will be processed, and it correlates with the probability of finding a corresponding fingerprint in the reference dataset. The deposition of fingermarks on random surfaces occurs spontaneously in an uncontrolled fashion, which introduces imperfections to the resulting impression of the friction ridge pattern. In this work, we propose a new probabilistic framework for Automated Fingermark Quality Assessment (AFQA). We used modern deep learning techniques, which have the ability to extract patterns even from noisy data, and combined them with a methodology from the field of eXplainable AI (XAI) to make our models more transparent. Our solution first predicts a quality probability distribution, from which we then calculate the final quality value and, if needed, the uncertainty of the model. Additionally, we complemented the predicted quality value with a corresponding quality map. We used GradCAM to determine which regions of the fingermark had the largest effect on the overall quality prediction. We show that the resulting quality maps are highly correlated with the density of minutiae points in the input image. Our deep learning approach achieved high regression performance, while significantly improving the interpretability and transparency of the predictions. Full article
(This article belongs to the Special Issue Trustless Biometric Sensors and Systems)
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