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Electronics
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Recent Advances in Biometric Security in IoT Based on Machine...
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Recent Advances in Biometric Security in IoT Based on Machine Learning

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 4528

Special Issue Editors

Prof. Dr. Nima Karimian

E-Mail Website
Guest Editor
Computer Engineering Department, San José State University, San Jose, CA 95192, USA
Interests: biometric security; IoT security; hardware security; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tempestt Neal

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of South Florida (USF), Tampa, FL 33620, USA
Interests: scalable biometrics; mobile biometrics; computer vision; authorship attribution; human-centered authentication; soft biometric classification; emotion recognition

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) applications has been deployed in a wide variety of critical infrastructure and applications ranging from transportation, healthcare, and supply chain. While IoT brings a number of benefits including convenience and efficiency, it also introduces a number of emerging threats. With the emergence of the Internet-of-Things (IoT), there is a growing need for access control and data protection. Biometric-based authentication is promising for IoT due to its convenient nature and lower susceptibility to attacks. Additionally, machine learning and deep learning techniques are delivering a promising solution to biometric systems and to increase the accuracy and plays a decisive role for presentation attack detection.

The goal of this special issue is to solicit high quality contributions on: (i) investigating the usage of deep learning and biometric systems in the context of IoT applications; (ii) novel techniques in biometric deep fakes and digital data forensics, particularly by exploiting, but not limited to, deep learning approaches.

Prof. Dr. Nima Karimian
Prof. Dr. Tempestt Neal
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. Electronics 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

  • Biometrics in Healthcare
  • Internet of Biometric Things (IoBT)
  • Anti-spoofing, Presentation Attack Detection in Biometric
  • Deep Learning Methods for Biometrics
  • Continuous Biometrics Authentication
  • Mobile-based Biometrics

Published Papers (1 paper)

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Research

22 pages, 1500 KiB  
Article
DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition
by Ahmad S. Tarawneh, Ahmad B. Hassanat, Esra’a Alkafaween, Bayan Sarayrah, Sami Mnasri, Ghada A. Altarawneh, Malek Alrashidi, Mansoor Alghamdi and Abdullah Almuhaimeed
Electronics 2022, 11(4), 513; https://doi.org/10.3390/electronics11040513 - 9 Feb 2022
Cited by 15 | Viewed by 3416
Abstract
Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. [...] Read more.
Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different layers and performing dimensionality reduction using principal component analysis (PCA), the extracted deep features are put to the test. The proposed system’s performance is assessed using experiments on the Delhi Finger Knuckle Dataset employing a variety of common classifiers. The best identification result was obtained when the Artificial neural network (ANN) classifier was applied to the principal components of the averaged feature vector of F6 and F7 deep features, with 95% of the data variance preserved. The findings also demonstrate the feasibility of employing these deep features in an FKP recognition system. Full article
(This article belongs to the Special Issue Recent Advances in Biometric Security in IoT Based on Machine Learning)
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