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Electronics
Special Issues
Biometrics and Pattern Recognition
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Biometrics and Pattern Recognition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 15 February 2025 | Viewed by 1396

Special Issue Editor

Dr. Cheng Yaw Low

E-Mail Website
Guest Editor
Institute for Basic Science, Yonsei University, Seoul, Republic of Korea
Interests: machine learning; pattern recognition; deep neural networks; stacking-based deep neural networks

Special Issue Information

Dear Colleagues,

The global market size of biometric technology has experienced significant growth, and this trend is expected to continue expanding in the coming years. This is attributed to the increasing utilization of biometric characteristics, e.g., face, fingerprint, voice, etc., in consumer electronics and automotive sectors, fueling a strong demand for user authentication, identification, and security and surveillance solutions. Hence, pattern recognition, a rapidly evolving field, drives the advancements in machine learning and extends its influence to computer vision, including biometrics for signal/image/video processing and analysis.

Topics of interests include, but are not limited to, the following:

  • Machine learning (ML) and embedding learning using deep learning (including lightweight architecture), and enhanced ML algorithms for biometrics and pattern recognition.
  • Biometrics, all physiological and behavioral attributes, including multimodal biometrics, and other relevant object recognition tasks, particularly those in the realistic open-set deployment setting.
  • Real-world and future artificial intelligence (AI) and ML systems/applications/surveys, such as security and monitoring, forensics, continuous authentication, law enforcement, aviation security, healthcare, transportation, smart homes, etc.
  • Fairness, accountability, transparency, and ethical (FATE) principles for AI and ML systems, especially biometrics.

Technical Program Committee Member:

Name: Dr. Kian Ming Lim
Email: [email protected]
Affiliation: School of Computer Science at the University of Nottingham Ningbo China, Ningbo 315100, China
Research Interests: machine learning; deep learning; computer vision; pattern recognition

Dr. Cheng Yaw Low
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 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. Electronics 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 2400 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

  • pattern recognition
  • biometrics recognition
  • computer vision
  • machine learning
  • signal/image/video processing and analysis

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Published Papers (2 papers)

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Research

22 pages, 9192 KiB  
Article
A Deep-Learning-Driven Aerial Dialing PIN Code Input Authentication System via Personal Hand Features
by Jun Wang, Haojie Wang, Kiminori Sato and Bo Wu
Electronics 2025, 14(1), 119; https://doi.org/10.3390/electronics14010119 - 30 Dec 2024
Viewed by 406
Abstract
The dialing-type authentication as a common PIN code input system has gained popularity due to the simple and intuitive design. However, this type of system has the security risk of “shoulder surfing attack”, so that attackers can physically view the device screen and [...] Read more.
The dialing-type authentication as a common PIN code input system has gained popularity due to the simple and intuitive design. However, this type of system has the security risk of “shoulder surfing attack”, so that attackers can physically view the device screen and keypad to obtain personal information. Therefore, based on the use of “Leap Motion” device and “Media Pipe” solutions, in this paper, we try to propose a new two-factor dialing-type input authentication system powered by aerial hand motions and features without contact. To be specific, based on the design of the aerial dialing system part, as the first authentication part, we constructed a total of two types of hand motion input subsystems using Leap Motion and Media Pipe, separately. The results of FRR (False Rejection Rate) and FAR (False Acceptance Rate) experiments of the two subsystems show that Media Pipe is more comprehensive and superior in terms of applicability, accuracy, and speed. Moreover, as the second authentication part, the user’s hand features (e.g., proportional characteristics associated with fingers and palm) were used for specialized CNN-LSTM model training to ultimately obtain a satisfactory accuracy. Full article
(This article belongs to the Special Issue Biometrics and Pattern Recognition)
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19 pages, 3563 KiB  
Article
Impact of Histogram Equalization on the Classification of Retina Lesions from OCT B-Scans
by Tomasz Marciniak and Agnieszka Stankiewicz
Electronics 2024, 13(24), 4996; https://doi.org/10.3390/electronics13244996 - 19 Dec 2024
Viewed by 463
Abstract
Deep learning solutions can be used to classify pathological changes of the human retina visualized in OCT images. Available datasets that can be used to train neural network models include OCT images (B-scans) of classes with selected pathological changes and images of the [...] Read more.
Deep learning solutions can be used to classify pathological changes of the human retina visualized in OCT images. Available datasets that can be used to train neural network models include OCT images (B-scans) of classes with selected pathological changes and images of the healthy retina. These images often require correction due to improper acquisition or intensity variations related to the type of OCT device. This article provides a detailed assessment of the impact of preprocessing on classification efficiency. The histograms of OCT images were examined and, depending on the histogram distribution, incorrect image fragments were removed. At the same time, the impact of histogram equalization using the standard method and the Contrast-Limited Adaptive Histogram Equalization (CLAHE) method was analyzed. The most extensive dataset of Labeled Optical Coherence Tomography (LOCT) images was used for the experimental studies. The impact of changes was assessed for different neural network architectures and various learning parameters, assuming classes of equal size. Comprehensive studies have shown that removing unnecessary white parts from the input image combined with CLAHE improves classification accuracy up to as much as 4.75% depending on the used network architecture and optimizer type. Full article
(This article belongs to the Special Issue Biometrics and Pattern Recognition)
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