Optimal Iris Fuzzy Sketches
J. Bringer, H. Chabanne, G. Cohen, B. Kindarji, G. Z\'emor
TL;DR
This paper explores the use of two-dimensional iterative min-sum decoding for iris fuzzy sketches, achieving near-optimal error correction performance validated on the ICE database.
Contribution
It introduces a novel decoding approach for iris fuzzy sketches that approaches theoretical limits, validated through experiments on a standard biometric database.
Findings
Near-theoretical limit error correction achieved
Effective decoding method validated on ICE database
Improved iris biometric matching accuracy
Abstract
Fuzzy sketches, introduced as a link between biometry and cryptography, are a way of handling biometric data matching as an error correction issue. We focus here on iris biometrics and look for the best error-correcting code in that respect. We show that two-dimensional iterative min-sum decoding leads to results near the theoretical limits. In particular, we experiment our techniques on the Iris Challenge Evaluation (ICE) database and validate our findings.
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