Editorial: Biometric Recognition—Latest Advances and Prospects

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

• Ref. [1] introduces a prior structure-assisted network for identity-preserving face animation, leveraging segmentation and landmarks to enhance realism under motion transfer.
• Ref. [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

• Ref. [3] adapts the Segment Anything Model with a novel “IrisAdapter” for high-precision segmentation, overcoming domain gaps between natural and iris images.
• Ref. [4] achieves efficient multi-modal ocular segmentation (periocular/sclera/iris/pupil) in noisy environments via shape priors and cross-attention.
• Ref. [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

• Ref. [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.
• Ref. [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

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

1.5. Security and Ethical Considerations

• Ref. [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:

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Cross-spectral integration (e.g., visible/infrared/multi-spectral fusion) will drive seamless authentication in non-cooperative environments.

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Lightweight, explainable AI must evolve to balance accuracy with ethical imperatives, addressing bias, privacy, and adversarial threats.

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Metaverse-ready biometrics will require novel sensors and generative models for immersive identity verification.

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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.