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Special Issue "Biometric Sensing"

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

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editors

Dr. Marcin Kowalski
Website
Guest Editor
Military University of Technology, Warsaw, Poland
Interests: biometric recognition (cross-spectral face recognition, multispectral counter spoofing); object detection (concealed object detection); computer vision (image classification)
Prof. James Ferryman
Website
Guest Editor
University of Reading, Reading, UK
Interests: computer vision; biometrics; surveillance

Special Issue Information

Biometric recognition systems are becoming increasingly popular. Measuring the physical or behavioral properties of a subject and detecting spoofing attacks require specific sensors operating across a wide spectrum of radiation. While some biometric modalities require simple sensors to capture samples, others use complicated equipment. These sensors may either output the raw signal or additionally convert the raw signal to the feature domain to distinguish individuals.

This Special Issue aims to highlight advances in biometric sensing as this relates to biometric recognition. MDPI’s Sensors solicits paper submissions and aims to bring together researchers and application developers on any of the critical issues involved. This Special Issue will cover a wide range of topics including works on new approaches to biometric sensing, multimodal sensors, biometric sensors’ performance assessment, as well as new concepts for vulnerability detection.

Dr. Marcin Kowalski
Prof. James Ferryman
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. 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 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

  • biometric sensors
  • biometrics modality
  • biometric recognition
  • face sensing
  • vascular sensors
  • multispectral biometrics
  • anthropometrics
  • fingerprint sensing
  • liveness sensing
  • counter spoofing detection

Published Papers (5 papers)

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Research

Open AccessArticle
Contact-Free Multispectral Identity Verification System Using Palm Veins and Deep Neural Network
Sensors 2020, 20(19), 5695; https://doi.org/10.3390/s20195695 - 06 Oct 2020
Abstract
Devices and systems secured by biometric factors became a part of our lives because they are convenient, easy to use, reliable, and secure. They use information about unique features of our bodies in order to authenticate a user. It is possible to enhance [...] Read more.
Devices and systems secured by biometric factors became a part of our lives because they are convenient, easy to use, reliable, and secure. They use information about unique features of our bodies in order to authenticate a user. It is possible to enhance the security of these devices by adding supplementary modality while keeping the user experience at the same level. Palm vein systems are based on infrared wavelengths used for capturing images of users’ veins. It is both convenient for the user, and it is one of the most secure biometric solutions. The proposed system uses IR and UV wavelengths; the images are then processed by a deep convolutional neural network for extraction of biometric features and authentication of users. We tested the system in a verification scenario that consisted of checking if the images collected from the user contained the same biometric features as those in the database. The True Positive Rate (TPR) achieved by the system when the information from the two modalities were combined was 99.5% by the threshold of acceptance set to the Equal Error Rate (EER). Full article
(This article belongs to the Special Issue Biometric Sensing)
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Open AccessArticle
Logical Attacks and Countermeasures for Fingerprint On-Card-Comparison Systems
Sensors 2020, 20(18), 5410; https://doi.org/10.3390/s20185410 - 21 Sep 2020
Abstract
Digital fingerprints are being used more and more to secure applications for logical and physical access control. In order to guarantee security and privacy trends, a biometric system is often implemented on a secure element to store the biometric reference template and for [...] Read more.
Digital fingerprints are being used more and more to secure applications for logical and physical access control. In order to guarantee security and privacy trends, a biometric system is often implemented on a secure element to store the biometric reference template and for the matching with a probe template (on-card-comparison). In order to assess the performance and robustness against attacks of these systems, it is necessary to better understand which information could help an attacker successfully impersonate a legitimate user. The first part of the paper details a new attack based on the use of a priori information (such as the fingerprint classification, sensor type, image resolution or number of minutiae in the biometric reference) that could be exploited by an attacker. In the second part, a new countermeasure against brute force and zero effort attacks based on fingerprint classification given a minutiae template is proposed. These two contributions show how fingerprint classification could have an impact for attacks and countermeasures in embedded biometric systems. Experiments show interesting results on significant fingerprint datasets. Full article
(This article belongs to the Special Issue Biometric Sensing)
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Open AccessArticle
A Study on Presentation Attack Detection in Thermal Infrared
Sensors 2020, 20(14), 3988; https://doi.org/10.3390/s20143988 - 17 Jul 2020
Abstract
Face recognition systems face real challenges from various presentation attacks. New, more sophisticated methods of presentation attacks are becoming more difficult to detect using traditional face recognition systems. Thermal infrared imaging offers specific physical properties that may boost presentation attack detection capabilities. The [...] Read more.
Face recognition systems face real challenges from various presentation attacks. New, more sophisticated methods of presentation attacks are becoming more difficult to detect using traditional face recognition systems. Thermal infrared imaging offers specific physical properties that may boost presentation attack detection capabilities. The aim of this paper is to present outcomes of investigations on the detection of various face presentation attacks in thermal infrared in various conditions including thermal heating of masks and various states of subjects. A thorough analysis of presentation attacks using printed and displayed facial photographs, 3D-printed, custom flexible 3D-latex and silicone masks is provided. The paper presents the intensity analysis of thermal energy distribution for specific facial landmarks during long-lasting experiments. Thermalization impact, as well as varying the subject’s state due to physical effort on presentation attack detection are investigated. A new thermal face spoofing dataset is introduced. Finally, a two-step deep learning-based method for the detection of presentation attacks is presented. Validation results of a set of deep learning methods across various presentation attack instruments are presented. Full article
(This article belongs to the Special Issue Biometric Sensing)
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Open AccessArticle
Image-Based Somatotype as a Biometric Trait for Non-Collaborative Person Recognition at a Distance and On-The-Move
Sensors 2020, 20(12), 3419; https://doi.org/10.3390/s20123419 - 17 Jun 2020
Abstract
It has recently been shown in Re-Identification (Re-ID) work that full-body images of people reveal their somatotype, even after change in apparel. A significant advantage of this biometric trait is that it can easily be captured, even at a distance, as a full-body [...] Read more.
It has recently been shown in Re-Identification (Re-ID) work that full-body images of people reveal their somatotype, even after change in apparel. A significant advantage of this biometric trait is that it can easily be captured, even at a distance, as a full-body image of a person, taken by a standard 2D camera. In this work, full-body image-based somatotype is investigated as a novel soft biometric feature for person recognition at a distance and on-the-move. The two common scenarios of (i) identification and (ii) verification are both studied and evaluated. To this end, two different deep networks have been recruited, one for the identification and one for the verification scenario. Experiments have been conducted on popular, publicly available datasets and the results indicate that somatotype can indeed be a valuable biometric trait for identity recognition at a distance and on-the-move (and hence also suitable for non-collaborative individuals) due to the ease of obtaining the required images. This soft biometric trait can be especially useful under a wider biometric fusion scheme. Full article
(This article belongs to the Special Issue Biometric Sensing)
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Open AccessArticle
Towards Fingerprint Spoofing Detection in the Terahertz Range
Sensors 2020, 20(12), 3379; https://doi.org/10.3390/s20123379 - 15 Jun 2020
Abstract
Spoofing attacks using imitations of fingerprints of legal users constitute a serious threat. In this study, a terahertz time domain spectroscopy (TDS) setup in a reflection configuration was used for the non-intrusive detection of fingerprint spoofing. Herein, the skin structure of the finger [...] Read more.
Spoofing attacks using imitations of fingerprints of legal users constitute a serious threat. In this study, a terahertz time domain spectroscopy (TDS) setup in a reflection configuration was used for the non-intrusive detection of fingerprint spoofing. Herein, the skin structure of the finger pad is described with a focus on the outermost stratum corneum. We identified and characterized five representative spoofing materials and prepared thin and thick finger imitations. The complex refractive index of the materials was determined in TDS in the transmission configuration. For dataset collection, we selected a group of 16 adults of various ages and genders. The reflection results were analyzed both in the time (reflected signal) and frequency (reflectivity) domains. The measured signals were positively verified with the theoretical calculations. The signals corresponding to samples differ from the finger-related signals, which facilitates spoofing detection. Thanks to deconvolution, we provide a basic explanation of the observed phenomena. We propose two spoofing detection methods, predefined time–frequency features and deep learning based. The methods achieved high true detection rates of 87.9% and 98.8%. Our results show that the terahertz technology can be successfully applied for spoofing detection with high detection probability. Full article
(This article belongs to the Special Issue Biometric Sensing)
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