Editorial: Biometric Recognition—Latest Advances and Prospects

Yunlong Wang; Zhaofeng He; Caiyong Wang; Jianze Wei; Min Ren

doi:10.3390/electronics14153108

,

and

¹

New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

²

School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China

³

School of Intelligence Science and Technology, Beijing University of Civil Engineering and Architecture, Beijing 102616, China

⁴

Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China

Electronics2025, 14(15), 3108;https://doi.org/10.3390/electronics14153108

This article belongs to the Special Issue Biometric Recognition: Latest Advances and Prospects

Version Notes

Order Reprints

We are pleased to present this Special Issue of Electronics, dedicated to exploring cutting-edge advancements in Biometric Recognition. As digital identity becomes integral to the “Internet of Everything,” this collection showcases interdisciplinary innovations spanning sensor design, algorithmic robustness, multi-modal fusion, and real-world applications. Below, we summarize the key contributions and extend our gratitude to all participants involved.

1. Research Highlights

1.1. Face Recognition and Animation

Contribution 1 introduces a prior structure-assisted network for identity-preserving face animation, leveraging segmentation and landmarks to enhance realism under motion transfer.
Contribution 2 addresses asymmetric matching across heterogeneous models (e.g., ResNet vs. Transformer) using learnable anchors that are critical for edge-device deployment.

1.2. Ocular Biometrics

Contribution 3 adapts the Segment Anything Model with a novel “IrisAdapter” for high-precision segmentation, overcoming domain gaps between natural and iris images.
Contribution 4 achieves efficient multi-modal ocular segmentation (periocular/sclera/iris/pupil) in noisy environments via shape priors and cross-attention.
Contribution 5 produces a pioneering VR periocular dataset with periocular images acquired in VR environments, identities and abundant emotion annotations, enabling implicit authentication and affective computing studies.

1.3. Noise-Robust Authentication

Contribution 6 mitigate flicker noise and IR reflections in periocular images captured by head-mounted display devices, achieving 6.39% EER via reflection removal and SE blocks.
Contribution 7 enhance voice-based two-factor authentication (2FA) with a GMM-based web solution, optimizing thresholds for varying acoustic conditions.

1.4. Emerging Modalities and Applications

Contribution 8 design an open-environment multi-spectral palm vein system with supervised feature learning (<1% EER).
Contribution 9 produce ultra-wideband (UWB) based real-time leg movement recognition (95% accuracy), enabling seamless human–robot interactions.
Contribution 10 combine the CNN and Transformer (CrowdCCT) for weakly supervised crowd counting, excelling in hybrid feature fusion.

1.5. Security and Ethical Considerations

Contribution 11 critically review ML vulnerabilities in biometrics (e.g., data poisoning, deepfakes), urging defenses against adversarial threats.

2. Future Outlook

The work herein signals three key trajectories for biometrics:

●: Cross-spectral integration (e.g., visible/infrared/multi-spectral fusion) will drive seamless authentication in non-cooperative environments.
●: Lightweight, explainable AI must evolve to balance accuracy with ethical imperatives, addressing bias, privacy, and adversarial threats.
●: Metaverse-ready biometrics will require novel sensors and generative models for immersive identity verification.
●: Ethical guardianship must embed privacy-by-design principles and rigorous ethical frameworks in future advances to prevent the misuse of sensitive biological data.

We expect more collaborations to build trustworthy, inclusive, and ethically grounded biometric systems for an interconnected world.

Acknowledgments

We thank all contributing authors for their rigorous research advancing developments in biometrics community. We extend our thanks to peer reviewers whose expertise ensured the scientific quality and novelty of published works and Special Issue guest editors for their service in shaping this interdisciplinary discourse.

Conflicts of Interest

The authors declare no conflict of interest.

List of Contributions

1.: Zhao, G.; Xu, J.; Wang, X.; Yan, F.; Qiu, S. PSAIP: Prior Structure-Assisted Identity-Preserving Network for Face Animation. Electronics 2025, 14, 784. https://doi.org/10.3390/electronics14040784.
2.: Kim, J.; Ng, T.-S.; Teoh, A.B.J. Learnable Anchor Embedding for Asymmetric Face Recognition. Electronics 2025, 14, 455. https://doi.org/10.3390/electronics14030455.
3.: Jiang, J.; Zhang, Q.; Wang, C. SAM-Iris: A SAM-Based Iris Segmentation Algorithm. Electronics 2025, 14, 246. https://doi.org/10.3390/electronics14020246.
4.: Zhang, Y.; Wang, C.; Li, H.; Sun, X.; Tian, Q.; Zhao, G. OcularSeg: Accurate and Efficient Multi-Modal Ocular Segmentation in Non-Constrained Scenarios. Electronics 2024, 13, 1967. https://doi.org/10.3390/electronics13101967.
5.: Seok, C.; Park, Y.; Baek, J.; Lim, H.; Roh, J.-h.; Kim, Y.; Kim, S.; Lee, E.C. AffectiVR: A Database for Periocular Identification and Valence and Arousal Evaluation in Virtual Reality. Electronics 2024, 13, 4112. https://doi.org/10.3390/electronics13204112.
6.: Baek, J.; Park, Y.; Seok, C.; Lee, E.C. Noise-Robust Biometric Authentication Using Infrared Periocular Images Captured from a Head-Mounted Display. Electronics 2025, 14, 240. https://doi.org/10.3390/electronics14020240.
7.: Kamiński, K.A.; Dobrowolski, A.P.; Piotrowski, Z.; Ścibiorek, P. Enhancing Web Application Security: Advanced Biometric Voice Verification for Two-Factor Authentication. Electronics 2023, 12, 3791. https://doi.org/10.3390/electronics12183791.
8.: Wu, W.; Li, Y.; Zhang, Y.; Li, C. Identity Recognition System Based on Multi-Spectral Palm Vein Image. Electronics 2023, 12, 3503. https://doi.org/10.3390/electronics12163503.
9.: Noh, M.; Ahn, H.; Lee, S.C. Real-Time Human Movement Recognition Using Ultra-Wideband Sensors. Electronics 2024, 13, 1300. https://doi.org/10.3390/electronics13071300.
10.: Cai, Y.; Zhang, D. A Weakly Supervised Crowd Counting Method via Combining CNN and Transformer. Electronics 2024, 13, 5053. https://doi.org/10.3390/electronics13245053.
11.: Ghilom, M.; Latifi, S. The Role of Machine Learning in Advanced Biometric Systems. Electronics 2024, 13, 2667. https://doi.org/10.3390/electronics13132667.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).