Minutia Texture Cylinder Codes for fingerprint matching

TL;DR

This paper enhances fingerprint matching by transforming Minutia Cylinder Codes into a texture-based descriptor using STFT, leading to improved accuracy on standard datasets.

Contribution

It introduces Minutia Texture Cylinder Codes (MTCC), a novel modification of MCC that incorporates texture features, significantly improving matching performance.

Findings

MTCC outperforms traditional MCC on FVC datasets.

Texture features significantly impact fingerprint matching accuracy.

Proposed method achieves higher accuracy with fixed parameters.

Abstract

Minutia Cylinder Codes (MCC) are minutiae based fingerprint descriptors that take into account minutiae information in a fingerprint image for fingerprint matching. In this paper, we present a modification to the underlying information of the MCC descriptor and show that using different features, the accuracy of matching is highly affected by such changes. MCC originally being a minutia only descriptor is transformed into a texture descriptor. The transformation is from minutiae angular information to orientation, frequency and energy information using Short Time Fourier Transform (STFT) analysis. The minutia cylinder codes are converted to minutiae texture cylinder codes (MTCC). Based on a fixed set of parameters, the proposed changes to MCC show improved performance on FVC 2002 and 2004 data sets and surpass the traditional MCC performance.