Finger Recovery Transformer for Enhanced Incomplete Fingerprint Identification and Reconstruction

DOI

10.2991/978-94-6463-718-2_54How to use a DOI?

Keywords

Fingerprint reconstruction; incomplete fingerprints; biometric identification; Finger Recovery Transformer; transformer networks; latent fingerprints; hybrid models; privacy-preserving biometrics; fingerprint enhancement; real-time fingerprint recognition; multimodal biometrics; computational efficiency; deep learning; forensic applications; noisy environments; scalable biometrics; fingerprint datasets; domain adaptation; attention mechanisms; fingerprint reconstruction benchmarks

Abstract

Biometric identification systems face great challenges and limitations when relying on fingerprints due to the use of incomplete and degraded fingerprint data. In this paper, we present the Finger Recovery Transformer (FRT), a new framework to strengthen incomplete fingerprint identification and reconstruction. The FRT uses advanced transformer-based architectures that incorporate light-weight attention mechanisms to reconstruct the missing/degraded regions of fingerprints effectively. Because of pre-processing modules, hybrid reconstruction techniques, and domain adaptation layers integrated in one framework, the FRT outperforms varying datasets (e.g., latent and low-quality fingerprints). This is a scalable and computationally efficient framework that is resilient to real-world challenges, including noisy environments, limited computational resources, and multimodal biometrics consolidation. A thorough evaluation against the best contemporary methods shows that FRT surpasses competing methods in accuracy, speed, and flexibility. Ethical concerns are also tackled with built-in privacy-preserving capabilities for safe and responsible deployment. The proposed FRT system is a considerable improvement over existing fingerprint recognition systems with applications in forensics, border control, and security systems.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. Banupriya
AU  - M. K. Nivodhini
AU  - P. Vasuki
AU  - S. Thrisha
AU  - C. Vignesh
AU  - S. Yamini
PY  - 2025
DA  - 2025/05/23
TI  - Finger Recovery Transformer for Enhanced Incomplete Fingerprint Identification and Reconstruction
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 621
EP  - 632
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_54
DO  - 10.2991/978-94-6463-718-2_54
ID  - Banupriya2025
ER  -