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January 1, 2026International Journal of Biometrics

ML-based fingerprint compression at higher rates: balancing visual fidelity and biometric accuracy

Ala Walie Eddine Boudersa Nadia Bouteraa Abdelghani Redjati Mohamed Boughazi

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Key Points

The aim is to investigate how machine learning can compress fingerprints efficiently while maintaining quality and accuracy.
Developed a machine learning algorithm for fingerprint compression.
Tested the algorithm at various compression rates.
Evaluated visual fidelity and biometric accuracy through performance metrics.
Achieved significant compression rates while preserving high visual fidelity.
Biometric accuracy remains above industry standards at higher data rates.
Demonstrated a trade-off between compression and fidelity that can be optimized.

Implication

Demonstrates the balance between visual fidelity and biometric accuracy in ML-based fingerprint compression, suggesting improved data handling.

Abstract

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Cite This Study

Boudersa et al. (2026) studied this question.

synapsesocial.com/papers/69aa6ee2531e4c4a9ff59198 https://doi.org/10.1504/ijbm.2026.10076705

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