https://doi.org/10.1140/epjp/s13360-026-07296-2
Review
A review on advancing biometric authentication through integrating multimodal fusion with synthetic data augmentation for adaptive systems
1
School of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, India
2
Computer Science and Engineering, ABES Institute of Technology, Ghaziabad, India
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Received:
26
August
2025
Accepted:
3
January
2026
Published online:
16
January
2026
Abstract
Biometric authentication systems are increasingly adopted in critical domains such as smart healthcare, IoT, and finance, where robust, privacy-preserving, and inclusive identity verification is essential. While multimodal, cross-modal, and sequential biometric fusion techniques have significantly improved authentication accuracy and resilience, their effectiveness is often constrained by data scarcity, demographic bias, and privacy regulations. This paper presents a comprehensive review of synthetic data augmentation approaches enabled by generative models such as GANs, diffusion models, and simulation-based frameworks and examines their role in advancing biometric fusion systems. We introduce a systematic taxonomy of fusion strategies and synthetic data generation paradigms, and critically analyze how existing synthetic augmentation techniques enhance fusion performance across diverse scenarios, including modality alignment, adversarial robustness, and adaptive authentication. Furthermore, we identify key limitations in current literature, such as domain mismatch, underexplored biometric modalities, and ethical considerations, and outline future research directions for developing scalable, fair, and secure biometric authentication systems. By consolidating recent advances at the intersection of generative AI and biometric fusion, this review provides a structured foundation for future research and real-world deployment.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2026
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
