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New Trends in Biometric Sensing and Information Processing
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New Trends in Biometric Sensing and Information Processing

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

Deadline for manuscript submissions: 20 November 2024 | Viewed by 2007

Special Issue Editors

Prof. Dr. Liaojun Pang

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Xidian University, Xi’an 710071, China
Interests: biometrics and encryption; network and information security; computer forensics
Dr. Zhicheng Cao
*
E-Mail Website
Guest Editor
Department of Biomedical Engineering, Xidian University, Xi’an 710071, China
Interests: biometrics; machine learning; computer vision; intelligent medical instrumentation
* Asst. Prof. Dr.
Prof. Dr. Natalia Schmid

E-Mail Website
Guest Editor
Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA
Interests: detection and estimation; statistical signal processing; biometrics

Special Issue Information

Dear Colleagues,

Biometrics is a fast-expanding and continuously evolving technology of the twenty-first century. Recent successes in electronics and computing stimulated the emergence of new sensing technologies, which in turn further expanded and accelerated the evolution of the already fast-growing field of biometrics. New sensors, modalities, algorithms, systems, and applications are bound to extend the current biometric technology to a future that will witness unprecedented identification performance, universality, reliability, privacy and security. 

The scope of this Special Issue includes emerging trends and recent advances in sensing-related technologies in application to biometrics. We suggest the following topics as the main research and development venues for this Special Issue.

- New sensor technologies with application to biometrics;

- Wireless sensor networks for biometrics;

- Internet of Things in application to biometrics;

- New imaging modalities and technologies for biometrics;

- New information processing methods for biometrics;

- New machine learning algorithms for biometrics;

- New systems and instruments for biometrics;

- New applications of biometric sensing technologies;

- Other emerging sensing technologies for biometrics.

Prof. Dr. Liaojun Pang
Dr. Zhicheng Cao
Prof. Dr. Natalia Schmid
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 modalities
  • sensing
  • signal processing
  • authentication and recognition
  • machine learning
  • deep learning
  • security
  • privacy

Published Papers (2 papers)

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Research

11 pages, 2197 KiB  
Communication
Enhancing Resilience in Biometric Research: Generation of 3D Synthetic Face Data Using Advanced 3D Character Creation Techniques from High-Fidelity Video Games and Animation
by Florian Erwin Blümel, Mathias Schulz, Ralph Breithaupt, Norbert Jung and Robert Lange
Sensors 2024, 24(9), 2750; https://doi.org/10.3390/s24092750 - 25 Apr 2024
Viewed by 187
Abstract
Biometric authentication plays a vital role in various everyday applications with increasing demands for reliability and security. However, the use of real biometric data for research raises privacy concerns and data scarcity issues. A promising approach using synthetic biometric data to address the [...] Read more.
Biometric authentication plays a vital role in various everyday applications with increasing demands for reliability and security. However, the use of real biometric data for research raises privacy concerns and data scarcity issues. A promising approach using synthetic biometric data to address the resulting unbalanced representation and bias, as well as the limited availability of diverse datasets for the development and evaluation of biometric systems, has emerged. Methods for a parameterized generation of highly realistic synthetic data are emerging and the necessary quality metrics to prove that synthetic data can compare to real data are open research tasks. The generation of 3D synthetic face data using game engines’ capabilities of generating varied realistic virtual characters is explored as a possible alternative for generating synthetic face data while maintaining reproducibility and ground truth, as opposed to other creation methods. While synthetic data offer several benefits, including improved resilience against data privacy concerns, the limitations and challenges associated with their usage are addressed. Our work shows concurrent behavior in comparing semi-synthetic data as a digital representation of a real identity with their real datasets. Despite slight asymmetrical performance in comparison with a larger database of real samples, a promising performance in face data authentication is shown, which lays the foundation for further investigations with digital avatars and the creation and analysis of fully synthetic data. Future directions for improving synthetic biometric data generation and their impact on advancing biometrics research are discussed. Full article
(This article belongs to the Special Issue New Trends in Biometric Sensing and Information Processing)
16 pages, 3578 KiB  
Article
LFLDNet: Lightweight Fingerprint Liveness Detection Based on ResNet and Transformer
by Kang Zhang, Shu Huang, Eryun Liu and Heng Zhao
Sensors 2023, 23(15), 6854; https://doi.org/10.3390/s23156854 - 01 Aug 2023
Cited by 1 | Viewed by 1252
Abstract
With the rapid development of fingerprint recognition systems, fingerprint liveness detection is gradually becoming regarded as the main countermeasure to protect the fingerprint identification system from spoofing attacks. Convolutional neural networks have shown great potential in fingerprint liveness detection. However, the generalization ability [...] Read more.
With the rapid development of fingerprint recognition systems, fingerprint liveness detection is gradually becoming regarded as the main countermeasure to protect the fingerprint identification system from spoofing attacks. Convolutional neural networks have shown great potential in fingerprint liveness detection. However, the generalization ability of the deep network model for unknown materials, and the computational complexity of the network, need to be further improved. A new lightweight fingerprint liveness detection network is here proposed to distinguish fake fingerprints from real ones. The method includes mainly foreground extraction, fingerprint image blocking, style transfer based on CycleGan and an improved ResNet with multi-head self-attention mechanism. The proposed method can effectively extract ROI and obtain the end-to-end data structure, which increases the amount of data. For false fingerprints generated from unknown materials, the use of CycleGan network improves the model generalization ability. The introduction of Transformer with MHSA in the improved ResNet improves detection performance and reduces computing overhead. Experiments on the LivDet2011, LivDet2013 and LivDet2015 datasets showed that the proposed method achieves good results. For example, on the LivDet2015 dataset, our methods achieved an average classification error of 1.72 across all sensors, while significantly reducing network parameters, and the overall parameter number was only 0.83 M. At the same time, the experiment on small-area fingerprints yielded an accuracy of 95.27%. Full article
(This article belongs to the Special Issue New Trends in Biometric Sensing and Information Processing)
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