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Electronics
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Biometric Recognition: Latest Advances and Prospects, 2nd Edition
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Biometric Recognition: Latest Advances and Prospects, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 4396

Special Issue Editors

Dr. Yunlong Wang

E-Mail Website
Guest Editor
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Interests: biometrics recognition; computational imaging; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhaofeng He

E-Mail Website
Guest Editor
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: biometrics; computer vision; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Dr. Caiyong Wang

E-Mail Website
Guest Editor
School of Intelligence Science and Technology, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Interests: biometrics; computer vision; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Jianze Wei

E-Mail
Guest Editor
Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China
Interests: biometrics; image segmentation; computer vision; brain-like intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Min Ren

E-Mail Website
Guest Editor
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: biometrics; computer vision; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biometric recognition empowers a machine to automatically detect, capture, process, analyze, and recognize digital physiological or behavioral signals with advanced intelligence. Biometrics, such as the face, iris, and fingerprint recognition, have become digital identity proof for people to enter the “Internet of Everything”. Biometric recognition requires interdisciplinary research of science and technology involving optical engineering, mechanical engineering, electronic engineering, machine learning, pattern recognition, computer vision, digital image processing, signal analysis, cognitive science, neuroscience, human–computer interaction, and information security. This Special Issue aims to provide a platform for researchers from various fields to exchange recent advances in biometric recognition and present their novel research and the latest results dedicated to biometric recognition. It also strives to spur research in emerging directions.

Any area of biometrics is within the scope of this Special Issue, and possible areas of interest include (but are not limited to):

  • Latest advances in biometric sensor design and data collection;
  • Approaches for trustworthy biometrics (e.g., related topics in bias and fairness, privacy and security, robustness, explainability, transparency, etc.);
  • Biometric recognition aided with generative models;
  • Developments of multimodal or multispectral biometrics;
  • Human identification at a distance in less cooperative environments;
  • Open-set liveness detection, anti-spoofing in the wild;
  • Efficient biometric algorithms for light architectures (e.g., match-on-card/board, mobile platforms, etc.);
  • Biometric applications in Metaverse.

Submissions must not be published or presented in part or entirety or considered for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%) and must be referenced. All submitted papers will be peer-reviewed and accepted based on quality, originality, novelty, and relevance to the theme of the Special Issue. 

Dr. Yunlong Wang
Prof. Dr. Zhaofeng He
Dr. Caiyong Wang
Dr. Jianze Wei
Dr. Min Ren
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 250 words) can be sent to the Editorial Office for assessment.

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

  • biometric recognition
  • face
  • iris
  • fingerprint
  • palmprint
  • vein
  • voiceprint
  • gait
  • person re-identification
  • multi-modal
 

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Related Special Issue

Published Papers (2 papers)

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18 pages, 3160 KB  
Article
Unleashing the Power of Dense Uncertainty Embeddings for More Efficient and Accurate Iris Recognition
by Haoyan Jiang, Siqi Guo, Yunlong Wang and Caiyong Wang
Electronics 2026, 15(2), 328; https://doi.org/10.3390/electronics15020328 - 12 Jan 2026
Viewed by 240
Abstract
Pixelwise dense representations are more prevalent in the field of iris recognition, also known as iris templates or IrisCodes. Almost all previous works of this kind are deterministic. To be specific, pixel-level representations are exclusively derived from certain point-by-point modeling, including filter responses, [...] Read more.
Pixelwise dense representations are more prevalent in the field of iris recognition, also known as iris templates or IrisCodes. Almost all previous works of this kind are deterministic. To be specific, pixel-level representations are exclusively derived from certain point-by-point modeling, including filter responses, phase correlations, and ordinal relations. Moreover, the binary mask indicating valid iris regions is solely determined by a fixed threshold or the output of standalone segmentation and localization algorithms. Uncertainty in acquisition factors in the process of iris imagery formation is not considered. In this paper, we propose a simple yet effective plug-and-play building block termed dual dense uncertainty embedding (D2UE), which can be seamlessly incorporated into deep learning (DL) frameworks that extract dense representations for iris recognition. D2UE has two pathways wherein both take dense feature maps of the backbone network as input. One pathway of D2UE predicts a variance-scaling map (VSM) and then applies it to an adaptive threshold-masking operation on the iris image. The dynamic threshold for each pixel in this manner is dependent on not only the intensity distribution of the iris image but also each pixel’s low-level uncertainty. The other pathway of D2UE adopts an over-parameterization technique and extracts uncertainty-embedded dense representations (UEDRs) by modeling each pixel’s contextual uncertainty. Extensive experiments on several iris datasets demonstrate that recognition performance under both within-database and cross-database settings can be significantly improved by incorporating D2UE into the baseline method. By integrating D2UE into various deep learning frameworks and evaluating their performance across multiple datasets, the results demonstrate that D2UE can be seamlessly incorporated into diverse architectures and can significantly enhance their recognition capabilities. D2UE only incurs slight computational overhead while surpassing a few SOTA methods with a large backbone network and much more training budget. Full article
(This article belongs to the Special Issue Biometric Recognition: Latest Advances and Prospects, 2nd Edition)
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Review

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17 pages, 278 KB  
Review
Comparative Analysis of Passkeys (FIDO2 Authentication) on Android and iOS for GDPR Compliance in Biometric Data Protection
by Albert Carroll and Shahram Latifi
Electronics 2025, 14(20), 4018; https://doi.org/10.3390/electronics14204018 - 13 Oct 2025
Cited by 1 | Viewed by 3626
Abstract
Biometric authentication, such as facial recognition and fingerprint scanning, is now standard on mobile devices, offering secure and convenient access. However, the processing of biometric data is tightly regulated under the European Union’s General Data Protection Regulation (GDPR), where such data qualifies as [...] Read more.
Biometric authentication, such as facial recognition and fingerprint scanning, is now standard on mobile devices, offering secure and convenient access. However, the processing of biometric data is tightly regulated under the European Union’s General Data Protection Regulation (GDPR), where such data qualifies as “special category” personal data when used for uniquely identifying individuals. Compliance requires meeting strict conditions, including explicit consent and data protection by design. Passkeys, the modern name for FIDO2-based authentication credentials developed by the FIDO Alliance, enable passwordless login using public key cryptography. Its “match-on-device” architecture stores biometric data locally in secure hardware (e.g., Android’s Trusted Execution Environment, Apple’s Secure Enclave), potentially reducing the regulatory obligations associated with cloud-based biometric processing. This paper examines how Passkeys are implemented on Android and iOS platforms and their differences in architecture, API access, and hardware design, and how those differences affect compliance with the GDPR. Through a comparative analysis, we evaluate the extent to which each platform supports local processing, data minimization, and user control—key principles under GDPR. We find that while both platforms implement strong local protections, differences in developer access, trust models, and biometric isolation can influence the effectiveness and regulatory exposure of Passkeys deployment. These differences have direct implications for privacy risk, legal compliance, and implementation choices by app developers and service providers. Our findings highlight the need for platform-aware design and regulatory interpretation in the deployment of biometric authentication technologies. This work can help inform stakeholders, policymakers, and legal experts in drafting robust privacy and ethical policies—not only in the realm of biometrics but across AI technologies more broadly. By understanding platform-level implications, future frameworks can better align technical design with regulatory compliance and ethical standards. Full article
(This article belongs to the Special Issue Biometric Recognition: Latest Advances and Prospects, 2nd Edition)
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