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Electronics
Special Issues
Recent Advances and Applications of Machine Learning in Pattern Recognition
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Recent Advances and Applications of Machine Learning in Pattern Recognition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 844

Special Issue Editors

Dr. Verónica Vasconcelos

E-Mail Website
Guest Editor
Coimbra Institute of Engineering, Polytechnic University of Coimbra, 3045-093 Coimbra, Portugal
Interests: pattern recognition; machine learning; image processing; biomedical applications
Dr. Maryam Abbasi

E-Mail Website
Guest Editor
Applied Research Institute, Polytechnic of Coimbra, 3045-093 Coimbra, Portugal
Interests: artificial intelligence; bioinformatics; computational biology; pattern recognition; machine learning; multi-objective optimization algorithms

Special Issue Information

Dear Colleagues,

In recent years, pattern recognition has undergone remarkable development, driven above all by the use of machine learning techniques. These algorithms have been applied to various areas, such as medical image analysis, visual recognition, biometrics, remote sensing, communication, and even computer vision in autonomous vehicles, enabling the development of pattern recognition systems that have greater precision and efficiency. Recent approaches allow for the processing and analysis of large volumes of data and also promote the finding of innovative solutions to complex problems.

In this Special Issue, we invite researchers, academics, and practitioners to submit original research articles, reviews, and case studies that explore the recent advances in and applications of machine learning in pattern recognition.

Dr. Verónica Vasconcelos
Dr. Maryam Abbasi
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. 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
  • machine learning
  • deep learning
  • image processing
  • image segmentation
  • image detection
  • image classification
  • biometrics
  • computer vision

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Published Papers (1 paper)

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Research

16 pages, 7057 KiB  
Article
VRBiom: A New Periocular Dataset for Biometric Applications of Head-Mounted Display
by Ketan Kotwal, Ibrahim Ulucan, Gökhan Özbulak, Janani Selliah and Sébastien Marcel
Electronics 2025, 14(9), 1835; https://doi.org/10.3390/electronics14091835 - 30 Apr 2025
Viewed by 327
Abstract
With advancements in hardware, high-quality head-mounted display (HMD) devices are being developed by numerous companies, driving increased consumer interest in AR, VR, and MR applications. This proliferation of HMD devices opens up possibilities for a wide range of applications beyond entertainment. Most commercially [...] Read more.
With advancements in hardware, high-quality head-mounted display (HMD) devices are being developed by numerous companies, driving increased consumer interest in AR, VR, and MR applications. This proliferation of HMD devices opens up possibilities for a wide range of applications beyond entertainment. Most commercially available HMD devices are equipped with internal inward-facing cameras to record the periocular areas. Given the nature of these devices and captured data, many applications such as biometric authentication and gaze analysis become feasible. To effectively explore the potential of HMDs for these diverse use-cases and to enhance the corresponding techniques, it is essential to have an HMD dataset that captures realistic scenarios. In this work, we present a new dataset of periocular videos acquired using a virtual reality headset called VRBiom. The VRBiom, targeted at biometric applications, consists of 900 short videos acquired from 25 individuals recorded in the NIR spectrum. These 10 s long videos have been captured using the internal tracking cameras of Meta Quest Pro at 72 FPS. To encompass real-world variations, the dataset includes recordings under three gaze conditions: steady, moving, and partially closed eyes. We have also ensured an equal split of recordings without and with glasses to facilitate the analysis of eye-wear. These videos, characterized by non-frontal views of the eye and relatively low spatial resolutions (400×400), can be instrumental in advancing state-of-the-art research across various biometric applications. The VRBiom dataset can be utilized to evaluate, train, or adapt models for biometric use-cases such as iris and/or periocular recognition and associated sub-tasks such as detection and semantic segmentation. In addition to data from real individuals, we have included around 1100 presentation attacks constructed from 92 PA instruments. These PAIs fall into six categories constructed through combinations of print attacks (real and synthetic identities), fake 3D eyeballs, plastic eyes, and various types of masks and mannequins. These PA videos, combined with genuine (bona fide) data, can be utilized to address concerns related to spoofing, which is a significant threat if these devices are to be used for authentication. The VRBiom dataset is publicly available for research purposes related to biometric applications only. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Machine Learning in Pattern Recognition)
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