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Electronics
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Biometric Recognition: Latest Advances and Prospects
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Biometric Recognition: Latest Advances and Prospects

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 11162

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
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
Dr. Caiyong Wang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Interests: biometrics; computer vision; pattern recognition
Dr. Jianze Wei

E-Mail Website
Guest Editor
Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China
Interests: biometrics; image segmentation; computer vision; brain-like intelligence
Dr. Min Ren

E-Mail Website
Guest Editor
School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
Interests: biometrics; computer vision; pattern recognition

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 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 a range of frontiers 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 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

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

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Published Papers (6 papers)

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Research

16 pages, 8003 KiB  
Article
AffectiVR: A Database for Periocular Identification and Valence and Arousal Evaluation in Virtual Reality
by Chaelin Seok, Yeongje Park, Junho Baek, Hyeji Lim, Jong-hyuk Roh, Youngsam Kim, Soohyung Kim and Eui Chul Lee
Electronics 2024, 13(20), 4112; https://doi.org/10.3390/electronics13204112 - 18 Oct 2024
Viewed by 576
Abstract
This study introduces AffectiVR, a dataset designed for periocular biometric authentication and emotion evaluation in virtual reality (VR) environments. To maximize immersion in VR environments, interactions must be seamless and natural, with unobtrusive authentication and emotion recognition technologies playing a crucial role. This [...] Read more.
This study introduces AffectiVR, a dataset designed for periocular biometric authentication and emotion evaluation in virtual reality (VR) environments. To maximize immersion in VR environments, interactions must be seamless and natural, with unobtrusive authentication and emotion recognition technologies playing a crucial role. This study proposes a method for user authentication by utilizing periocular images captured by a camera attached to a VR headset. Existing datasets have lacked periocular images acquired in VR environments, limiting their practical application. To address this, periocular images were collected from 100 participants using the HTC Vive Pro and Pupil Labs infrared cameras in a VR environment. Participants also watched seven emotion-inducing videos, and emotional evaluations for each video were conducted. The final dataset comprises 1988 monocular videos and corresponding self-assessment manikin (SAM) evaluations for each experimental video. This study also presents a baseline study to evaluate the performance of biometric authentication using the collected dataset. A deep learning model was used to analyze the performance of biometric authentication based on periocular data collected in a VR environment, confirming the potential for implicit and continuous authentication. The high-resolution periocular images collected in this study provide valuable data not only for user authentication but also for emotion evaluation research. The dataset developed in this study can be used to enhance user immersion in VR environments and as a foundational resource for advancing emotion recognition and authentication technologies in fields such as education, therapy, and entertainment. This dataset offers new research opportunities for non-invasive continuous authentication and emotion recognition in VR environments, and it is expected to significantly contribute to the future development of related technologies. Full article
(This article belongs to the Special Issue Biometric Recognition: Latest Advances and Prospects)
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10 pages, 178 KiB  
Article
The Role of Machine Learning in Advanced Biometric Systems
by Milkias Ghilom and Shahram Latifi
Electronics 2024, 13(13), 2667; https://doi.org/10.3390/electronics13132667 - 7 Jul 2024
Viewed by 1763
Abstract
Today, the significance of biometrics is more pronounced than ever in accurately allowing access to valuable resources, from personal devices to highly sensitive buildings, as well as classified information. Researchers are pushing forward toward devising robust biometric systems with higher accuracy, fewer false [...] Read more.
Today, the significance of biometrics is more pronounced than ever in accurately allowing access to valuable resources, from personal devices to highly sensitive buildings, as well as classified information. Researchers are pushing forward toward devising robust biometric systems with higher accuracy, fewer false positives and false negatives, and better performance. On the other hand, machine learning (ML) has been shown to play a key role in improving such systems. By constantly learning and adapting to users’ changing biometric patterns, ML algorithms can improve accuracy and performance over time. The integration of ML algorithms with biometrics, however, introduces vulnerabilities in such systems. This article investigates the new issues of concern that come about because of the adoption of ML methods in biometric systems. Specifically, techniques to breach biometric systems, namely, data poisoning, model inversion, bias injection, and deepfakes, are discussed. Here, the methodology consisted of conducting a detailed review of the literature in which ML techniques have been adopted in biometrics. In this study, we included all works that have successfully applied ML and reported favorable results after this adoption. These articles not only reported improved numerical results but also provided sound technical justification for this improvement. There were many isolated, unsupported, and unjustified works about the major advantages of ML techniques in improving security, which were excluded from this review. Though briefly mentioned, we did not touch upon encryption/decryption aspects, and, accordingly, cybersecurity was excluded from this study. At the end, recommendations are made to build stronger and more secure systems that benefit from ML adoption while closing the door to adversarial attacks. Full article
(This article belongs to the Special Issue Biometric Recognition: Latest Advances and Prospects)
23 pages, 4201 KiB  
Article
OcularSeg: Accurate and Efficient Multi-Modal Ocular Segmentation in Non-Constrained Scenarios
by Yixin Zhang, Caiyong Wang, Haiqing Li, Xianyun Sun, Qichuan Tian and Guangzhe Zhao
Electronics 2024, 13(10), 1967; https://doi.org/10.3390/electronics13101967 - 17 May 2024
Viewed by 1221
Abstract
Multi-modal ocular biometrics has recently garnered significant attention due to its potential in enhancing the security and reliability of biometric identification systems in non-constrained scenarios. However, accurately and efficiently segmenting multi-modal ocular traits (periocular, sclera, iris, and pupil) remains challenging due to noise [...] Read more.
Multi-modal ocular biometrics has recently garnered significant attention due to its potential in enhancing the security and reliability of biometric identification systems in non-constrained scenarios. However, accurately and efficiently segmenting multi-modal ocular traits (periocular, sclera, iris, and pupil) remains challenging due to noise interference or environmental changes, such as specular reflection, gaze deviation, blur, occlusions from eyelid/eyelash/glasses, and illumination/spectrum/sensor variations. To address these challenges, we propose OcularSeg, a densely connected encoder–decoder model incorporating eye shape prior. The model utilizes Efficientnetv2 as a lightweight backbone in the encoder for extracting multi-level visual features while minimizing network parameters. Moreover, we introduce the Expectation–Maximization attention (EMA) unit to progressively refine the model’s attention and roughly aggregate features from each ocular modality. In the decoder, we design a bottom-up dense subtraction module (DSM) to amplify information disparity between encoder layers, facilitating the acquisition of high-level semantic detailed features at varying scales, thereby enhancing the precision of detailed ocular region prediction. Additionally, boundary- and semantic-guided eye shape priors are integrated as auxiliary supervision during training to optimize the position, shape, and internal topological structure of segmentation results. Due to the scarcity of datasets with multi-modal ocular segmentation annotations, we manually annotated three challenging eye datasets captured in near-infrared and visible light scenarios. Experimental results on newly annotated and existing datasets demonstrate that our model achieves state-of-the-art performance in intra- and cross-dataset scenarios while maintaining efficient execution. Full article
(This article belongs to the Special Issue Biometric Recognition: Latest Advances and Prospects)
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22 pages, 5063 KiB  
Article
Real-Time Human Movement Recognition Using Ultra-Wideband Sensors
by Minseong Noh, Heungju Ahn and Sang C. Lee
Electronics 2024, 13(7), 1300; https://doi.org/10.3390/electronics13071300 - 30 Mar 2024
Viewed by 1460
Abstract
This study introduces a methodology for the real-time detection of human movement based on two legs using ultra-wideband (UWB) sensors. Movements were primarily categorized into four states: stopped, walking, lingering, and the transition between sitting and standing. To classify these movements, UWB sensors [...] Read more.
This study introduces a methodology for the real-time detection of human movement based on two legs using ultra-wideband (UWB) sensors. Movements were primarily categorized into four states: stopped, walking, lingering, and the transition between sitting and standing. To classify these movements, UWB sensors were used to measure the distance between the designated point and a specific point on the two legs in the human body. By analyzing the measured distance values, a movement state classification model was constructed. In comparison to conventional vision/laser/LiDAR-based research, this approach requires fewer computational resources and provides distinguished real-time human movement detection within a CPU environment. Consequently, this research presents a novel strategy to effectively recognize human movements during human–robot interactions. The proposed model effectively discerned four distinct movement states with classification accuracy of around 95%, demonstrating the novel strategy’s efficacy. Full article
(This article belongs to the Special Issue Biometric Recognition: Latest Advances and Prospects)
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19 pages, 4013 KiB  
Article
Enhancing Web Application Security: Advanced Biometric Voice Verification for Two-Factor Authentication
by Kamil Adam Kamiński, Andrzej Piotr Dobrowolski, Zbigniew Piotrowski and Przemysław Ścibiorek
Electronics 2023, 12(18), 3791; https://doi.org/10.3390/electronics12183791 - 7 Sep 2023
Cited by 4 | Viewed by 1869
Abstract
This paper presents a voice biometrics system implemented in a web application as part of a two-factor authentication (2FA) user login. The web-based application, via a client interface, runs registration, preprocessing, feature extraction and normalization, classification, and speaker verification procedures based on a [...] Read more.
This paper presents a voice biometrics system implemented in a web application as part of a two-factor authentication (2FA) user login. The web-based application, via a client interface, runs registration, preprocessing, feature extraction and normalization, classification, and speaker verification procedures based on a modified Gaussian mixture model (GMM) algorithm adapted to the application requirements. The article describes in detail the internal modules of this ASR (Automatic Speaker Recognition) system. A comparison of the performance of competing ASR systems using the commercial NIST 2002 SRE voice dataset tested under the same conditions is also presented. In addition, it presents the results of the influence of the application of cepstral mean and variance normalization over a sliding window (WCMVN) and its relevance, especially for voice recordings recorded in varying acoustic tracks. The article also presents the results of the selection of a reference model representing an alternative hypothesis in the decision-making system, which significantly translates into an increase in the effectiveness of speaker verification. The final experiment presented is a test of the performance achieved in a varying acoustic environment during remote voice login to a web portal by the test group, as well as a final adjustment of the decision-making threshold. Full article
(This article belongs to the Special Issue Biometric Recognition: Latest Advances and Prospects)
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17 pages, 5933 KiB  
Article
Identity Recognition System Based on Multi-Spectral Palm Vein Image
by Wei Wu, Yunpeng Li, Yuan Zhang and Chuanyang Li
Electronics 2023, 12(16), 3503; https://doi.org/10.3390/electronics12163503 - 18 Aug 2023
Cited by 2 | Viewed by 2130
Abstract
A multi-spectral palm vein image acquisition device based on an open environment has been designed to achieve a highly secure and user-friendly biometric recognition system. Furthermore, we conducted a study on a supervised discriminative sparse principal component analysis algorithm that preserves the neighborhood [...] Read more.
A multi-spectral palm vein image acquisition device based on an open environment has been designed to achieve a highly secure and user-friendly biometric recognition system. Furthermore, we conducted a study on a supervised discriminative sparse principal component analysis algorithm that preserves the neighborhood structure for palm vein recognition. The algorithm incorporates label information, sparse constraints, and local information for effective supervised learning. By employing a robust neighborhood selection technique, it extracts discriminative and interpretable principal component features from non-uniformly distributed multi-spectral palm vein images. The algorithm addresses challenges posed by light scattering, as well as issues related to rotation, translation, scale variation, and illumination changes during non-contact image acquisition, which can increase intra-class distance. Experimental tests are conducted using databases from the CASIA, Tongji University, and Hong Kong Polytechnic University, as well as a self-built multi-spectral palm vein dataset. The results demonstrate that the algorithm achieves the lowest equal error rates of 0.50%, 0.19%, 0.16%, and 0.1%, respectively, using the optimal projection parameters. Compared to other typical methods, the algorithm exhibits distinct advantages and holds practical value. Full article
(This article belongs to the Special Issue Biometric Recognition: Latest Advances and Prospects)
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