Title

Design of cancelable MCC-based fingerprint templates using dyno-key model

Document Type

Journal Article

Publication Title

Pattern Recognition

Volume

119

Publisher

Elsevier

School

School of Science / ECU Security Research Institute

Comments

Bedari, A., Wang, S., & Yang, W. (2021). Design of cancelable MCC-based fingerprint templates using dyno-key model. Pattern Recognition, 119, article 108074. https://doi.org/10.1016/j.patcog.2021.108074

Abstract

Minutia Cylinder Code (MCC) is an effective, high-quality representation of local minutia structures. MCC templates demonstrate fast and excellent fingerprint matching performance, but if compromised, they can be reverse-engineered to retrieve minutia information. In this paper, we propose alignment-free cancelable MCC-based templates by exploiting the MCC feature extraction and representation. The core component of our design is a dynamic random key model, called Dyno-key model. The Dyno-key model dynamically extracts elements from MCC's binary feature vectors based on randomly generated keys. Those extracted elements are discarded after the block-based logic operations so as to increase security. Leveling with the performance of the unprotected, reproduced MCC templates, the proposed method exhibits competitive performance in comparison with state-of-the-art cancelable fingerprint templates, as evaluated over seven public databases, FVC2002 DB1-DB3, FVC2004 DB1 and DB2, and FVC2006 DB2 and DB3. The proposed cancelable MCC-based templates satisfy all the requirements of biometric template protection.

DOI

10.1016/j.patcog.2021.108074

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