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Special Issue "Biometric Sensors and Applications"

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

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editor

Dr. Javier Galbally
E-Mail Website
Guest Editor
European Commission - DG Joint Research Centre, Ispra, Italy
Interests: machine learning; biometrics; biometric quality; biometrics vulnerabilities; presentation attack detection; age effect; ageing effect; 3D fingerprint recognition; synthetic biometric generation; inverse biometrics

Special Issue Information

Dear Colleagues,

We are all well aware that, in biometrics, in many occasions, the success or failure of a given operation is largely determined by the very first step of the whole processing chain: acquisition. In this regard, the sensors used to capture the physical reality of natural biometric characteristics and translate it into the digital domain play a pivotal role in the accomplishment of the final goal. For instance, it is well known that recognition accuracy depends to a large extent on the quality of the acquired samples. According to the “fidelity definition”, given in ISO/IEC 29794-1, biometric quality can be understood as the degree in which the biometric sample represents the natural biometric characteristic (i.e., the source). Therefore, one of the key factors that determines biometric quality and, in turn, also biometric accuracy is how precisely the sensor is able to capture the source. This is just an illustrative example of the crucial impact that the development, deployment, and use of innovative high-quality sensors plays in the current and future progress of the biometric field.

However, in spite of their relevance, to date, the research effort dedicated to the study of sensor-related issues and to the production of new sensing solutions in biometrics still lags behind compared to pure software algorithmic development. In order to address this research gap, the present Special Issue (SI) is specifically dedicated to publishing innovative studies focused on the exploitation of new sensing technologies in the field of biometrics and also to the use of sensors to address new biometric applications and challenges.

In this context, the SI will cover original research works including (but not limited to): new sensors in any biometric characteristic, wearable sensors applied to biometrics, sensors for presentation attack detection (PAD), sensor interoperability studies, sensing solutions for biometrics at a distance, biometric sensing solutions for border crossings, sensor operation on mobile devices, biometric use of pervasive sensors, and multimodal acquisition.

Dr. Javier Galbally
Guest Editor

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 2200 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

  • Sensor interoperability
  • Presentation attack detection
  • Biometric recognition at a distance
  • Portable devices
  • Smart wearables
  • Smartphones
  • Border crossings/ABC gates
  • 3D/2D fingerprint sensors
  • 3D/2D face sensors
  • Iris sensors
  • Voice sensors
  • Gait sensors
  • Handwriting sensors
  • Oscilloscopes
  • Brainwaves sensors
  • Health sensors
  • Heartrate monitors
  • Pressure sensors
  • Laser sensors
  • VIS/NIR Imaging technologies
  • Ultrasounds
  • Thermal sensors
  • High-frequency sensors
  • 3D/2.5D/2D sensors

Published Papers (1 paper)

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Research

Open AccessArticle
Transformers and Generative Adversarial Networks for Liveness Detection in Multitarget Fingerprint Sensors
Sensors 2021, 21(3), 699; https://doi.org/10.3390/s21030699 - 20 Jan 2021
Viewed by 629
Abstract
Fingerprint-based biometric systems have grown rapidly as they are used for various applications including mobile payments, international border security, and financial transactions. The widespread nature of these systems renders them vulnerable to presentation attacks. Hence, improving the generalization ability of fingerprint presentation attack [...] Read more.
Fingerprint-based biometric systems have grown rapidly as they are used for various applications including mobile payments, international border security, and financial transactions. The widespread nature of these systems renders them vulnerable to presentation attacks. Hence, improving the generalization ability of fingerprint presentation attack detection (PAD) in cross-sensor and cross-material setting is of primary importance. In this work, we propose a solution based on a transformers and generative adversarial networks (GANs). Our aim is to reduce the distribution shift between fingerprint representations coming from multiple target sensors. In the experiments, we validate the proposed methodology on the public LivDet2015 dataset provided by the liveness detection competition. The experimental results show that the proposed architecture yields an increase in average classification accuracy from 68.52% up to 83.12% after adaptation. Full article
(This article belongs to the Special Issue Biometric Sensors and Applications)
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