Secure and Efficient Biometric-Data Binarization using Multi-Objective Optimization

Abstract

Biometric system databases are vulnerable to many types of attacks. To address this issue, several biometric template protection systems have been proposed to protect biometric data against unauthorized use. Many of biometric protection systems require the biometric templates to be represented in a binary form. Therefore, extracting binary templates from real-valued biometric data is a key step in such biometric data protection systems. In addition, binary representation of biometric data can speed-up the matching process and reduce the storage capacity required to store the enrolled templates. The main challenge of existing biometric data binarization approaches is to retain the discrimination power of the original real-valued templates after binarization. In this paper, we propose a secure and efficient biometric data binarization scheme that employs multi-objective optimization using Nondominated Sorting Genetic Algorithm (NSGA-II). The goal of the proposed method is to find optimal quantization and encoding parameters that are employed in the binarization process. Results obtained from the experiments conducted on the ORL face and MCYT fingerprint databases show a promising recognition accuracy without sacrificing the security of the system.

Keywords:

• Biometrics
• Binarization
• Quantization
• Encoding
• Multi-objective Optimization