Rank Correlation Measure: A Representational Transformation for Biometric Template Protection

TL;DR

This paper introduces a ranking-based transformation for fingerprint templates that enhances security and privacy by providing non-invertibility, cancellability, and robustness against noise, while maintaining reasonable recognition accuracy.

Contribution

It proposes a novel ranking-based representational transformation that improves biometric template security and robustness, addressing limitations of existing methods.

Findings

Achieves resilience to numeric noise and perturbations.

Provides strong non-invertible nonlinear embedding.

Demonstrates competitive accuracy on fingerprint benchmarks.

Abstract

Despite a variety of theoretical-sound techniques have been proposed for biometric template protection, there is rarely practical solution that guarantees non-invertibility, cancellability, non-linkability and performance simultaneously. In this paper, a ranking-based representational transformation is proposed for fingerprint templates. The proposed method transforms a real-valued feature vector into index code such that the pairwise-order measure in the resultant codes are closely correlated with rank similarity measure. Such a ranking based technique offers two major merits: 1) Resilient to noises/perturbations in numeric values; and 2) Highly nonlinear embedding based on partial order statistics. The former takes care of the accuracy performance mitigating numeric noises/perturbations while the latter offers strong non-invertible transformation via nonlinear feature embedding from Euclidean to Rank space that leads to toughness in inversion. The experimental results demonstrate reasonable accuracy performance on benchmark FVC2002 and FVC2004 fingerprint databases, thus confirm the proposition of the rank correlation. Moreover, the security and privacy analysis justify the strong capability against the existing major privacy attacks.