Special Issue "Advanced Machine Learning Algorithms for Biometrics and Its Applications"

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

Deadline for manuscript submissions: 30 April 2021.

Special Issue Editors

Dr. Larbi Boubchir
Website
Guest Editor
LIASD research Lab. – University of Paris 8, 2 Rue de la Liberté, 93526 Saint-Denis, France
Interests: biomedical signal processing; EEG; image processing; machine learning; brain–computer interface; biometrics
Special Issues and Collections in MDPI journals
Prof. Dr. Elhadj Benkhelifa
Website
Guest Editor
Cloud Computing and Applications Research Lab, School of Computing and Digital Technologies, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
Interests: cloud computing; cybersecurity; software engineering; software defined systems; cloud forensics; IoT; data governance
Prof. Dr. Boubaker Daachi
Website
Guest Editor
LIASD research Lab. – University of Paris 8, 2 Rue de la Liberté, 93526 Saint-Denis, France
Interests: robotics; soft computing; BCI; WSN; biometrics
Special Issues and Collections in MDPI journals

Special Issue Information

Biometrics has become a burgeoning research area due to the industrial and government needs for recognition and security concerns. It has also become a center of focus for many applications, such as identity authentication and identification in civil and forensic fields. Recently, advanced machine learning has received a great deal of attention in solving difficult and complex problems related to biometric recognition and security, where conventional machine learning techniques have shown their limitations.

This Special Issue aims to solicit original research papers, as well as review articles focusing on biometrics and its applications based on advanced machine learning algorithms. We are inviting original research works covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances in the biometrics domain.

In addition, the authors of the papers which will be presented at the 4th International Workshop on “Recent Advances in Biometrics and its Applications” that we are organizing in conjunction with the 43rd International Conference on Telecommunications and Signal Processing (TSP) will be invited to submit an extended version of their papers to this Special Issue after the conference. Submitted papers should be extended to the size of regular research or review articles, with at least a 50% extension of new results. There are no page limitations for this journal.

Topics of interest include but are not limited to the following:

  • Biometrics-based authentication and identification;
  • Physiological and behavioral biometrics (e.g., finger, palm, face, eye, ear, iris, retina, vein, gait, handwriting, voice);
  • Biometric feature extraction and matching;
  • Signal, image, and video processing in biometrics;
  • Advanced pattern recognition in biometrics;
  • Machine learning and deep learning in biometrics;
  • Artificial intelligence in biometrics;
  • Fusion techniques in biometrics;
  • Soft biometrics;
  • Multimodal biometrics;
  • Security and privacy in biometrics;
  • Big data challenges in biometrics;
  • Embedded biometric systems;
  • Emerging biometrics;
  • Related applications.
Dr. Larbi Boubchir
Prof. Dr. Elhadj Benkhelifa
Prof. Dr. Boubaker Daachi
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 papers will be 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 2000 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
  • machine learning
  • artificial intelligence
  • algorithm

Published Papers (1 paper)

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Research

Open AccessArticle
Biometrics Verification Modality Using Multi-Channel sEMG Wearable Bracelet
Appl. Sci. 2020, 10(19), 6960; https://doi.org/10.3390/app10196960 - 05 Oct 2020
Abstract
Electrical biosignals have the potential for use as biometric authenticators, owing to their ability to facilitate liveness detection and concealed nature. In this work, the viability of using surface electromyogram (sEMG) as a biometric modality for users verification is investigated. A database of [...] Read more.
Electrical biosignals have the potential for use as biometric authenticators, owing to their ability to facilitate liveness detection and concealed nature. In this work, the viability of using surface electromyogram (sEMG) as a biometric modality for users verification is investigated. A database of multi-channel sEMG signals is created using a wearable armband from able-bodied users. Each user used his/her muscles to form a password that consists of a unique combination of specific hand gestures. A total of 18 features are extracted from the signals in order to distinguish between the users. Several features are extracted in the frequency domain after estimating the power spectral density while using the Welch’s method. Specifically, average frequency, signal power, median frequency, Kurtosis, Deciles, coefficient of dissymmetry, and the peak frequency of the sEMG signal are considered. To further increase the accuracy of the classifier, time domain features are also extracted through segmentation of the signal into 10 segments, and then calculating both the root mean square and length of the signal. Several classifiers that are based on K-nearest Neighbors (KNN), Linear Discernment Analysis (LDA), and Ensemble of Classifiers are constructed, trained, and statistically compared, resulting in an average accuracy in 97.4%, 98.3%, and 98.5%, respectively. False acceptance rate (FAR) and False Rejection Rate (FRR) are estimated for each classifier in order to determine the effectiveness of the biometrics verification system. Although the ensemble classifier accuracy was found to be the highest, the results show that the KNN classifier exhibits a FAR of 0.2% and FRR of 2.9%. Thus, the KNN classifier was found to he the optimum classifier after the extraction of all 18 features. This work demonstrates the usefulness of sEMG as a biometric authenticator in user verification. Full article
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