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Recent Advance of Sensors and Algorithms for Biometrics
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Recent Advance of Sensors and Algorithms for Biometrics

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 5684

Special Issue Editors

Dr. Kien Nguyen Thanh

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Guest Editor
Science and Engineering Faculty, School of Electrical Engineering & Robotics, Queensland University of Technology, Brisbane, QLD, Australia
Interests: biometrics; computer vision; deep learning; artificial intelligence; surveillance
Dr. Satish Kumar Singh

E-Mail Website
Co-Guest Editor
Indian Institute of Information Technology Allahabad Jhalwa, Devghat, Prayagraj-211015 (UP), India
Interests: machine and deep learning; data compression; image processing; histopathology image analysis; biometrics; watermarking; retrieval; thermal imaging; visual sensor network; thermal object detection and recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last 5 years, our Sensors journal has published more than 100 papers on the biometric topic. This includes a wide range of biometric topics and modalities, including both physical traits such as face, fingerprint, iris, periocular, and behavioral traits such as keypress and gait. This demonstrates great interest in these areas from the researchers and readers of the Sensors journal. It is now an interesting time for the biometric and sensor communities, considering recent advances in manufacturing technologies and breakthroughs in computer vision and machine learning, especially with deep learning. The aim of this Special Issue is to bring together innovative applications of sensors and algorithms to advance the biometric field. Papers are welcome that address a wide range of applications of biometric sensors and algorithms, including, but not limited to, recent advances in deep learning, graphical modelling, memory networks, and transformers. Both review articles and original research papers relating to the application of biometric sensors and algorithms are solicited.

Dr. Kien Nguyen Thanh
Dr. Satish Kumar Singh
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 and algorithms
  • computer vision
  • deep learning
  • artificial intelligence
  • surveillance
  • graphical modelling

Published Papers (1 paper)

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Research

15 pages, 2698 KiB  
Article
Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units
by Luis Lopes Chambino, José Silvestre Silva and Alexandre Bernardino
Sensors 2021, 21(13), 4520; https://doi.org/10.3390/s21134520 - 1 Jul 2021
Cited by 10 | Viewed by 4784
Abstract
Facial recognition is a method of identifying or authenticating the identity of people through their faces. Nowadays, facial recognition systems that use multispectral images achieve better results than those that use only visible spectral band images. In this work, a novel architecture for [...] Read more.
Facial recognition is a method of identifying or authenticating the identity of people through their faces. Nowadays, facial recognition systems that use multispectral images achieve better results than those that use only visible spectral band images. In this work, a novel architecture for facial recognition that uses multiple deep convolutional neural networks and multispectral images is proposed. A domain-specific transfer-learning methodology applied to a deep neural network pre-trained in RGB images is shown to generalize well to the multispectral domain. We also propose a skin detector module for forgery detection. Several experiments were planned to assess the performance of our methods. First, we evaluate the performance of the forgery detection module using face masks and coverings of different materials. A second study was carried out with the objective of tuning the parameters of our domain-specific transfer-learning methodology, in particular which layers of the pre-trained network should be retrained to obtain good adaptation to multispectral images. A third study was conducted to evaluate the performance of support vector machines (SVM) and k-nearest neighbor classifiers using the embeddings obtained from the trained neural network. Finally, we compare the proposed method with other state-of-the-art approaches. The experimental results show performance improvements in the Tufts and CASIA NIR-VIS 2.0 multispectral databases, with a rank-1 score of 99.7% and 99.8%, respectively. Full article
(This article belongs to the Special Issue Recent Advance of Sensors and Algorithms for Biometrics)
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