Enhancing Template Security of Face Biometrics by Using Edge Detection and Hashing

TL;DR

This paper proposes a new method combining edge detection and hashing to create secure, revocable face biometric templates that resist tampering and cross-matching, enhancing privacy and security in biometric systems.

Contribution

It introduces a novel scheme using edge detection and hashing for secure, cancellable face biometric templates with improved verification against tampering.

Findings

Roberts Cross operator performs best among tested edge detection methods.

The proposed hashing scheme enhances template security and verification.

Edge detection-based templates resist cross-matching and tampering.

Abstract

In this paper we address the issues of using edge detection techniques on facial images to produce cancellable biometric templates and a novel method for template verification against tampering. With increasing use of biometrics, there is a real threat for the conventional systems using face databases, which store images of users in raw and unaltered form. If compromised not only it is irrevocable, but can be misused for cross-matching across different databases. So it is desirable to generate and store revocable templates for the same user in different applications to prevent cross-matching and to enhance security, while maintaining privacy and ethics. By comparing different edge detection methods it has been observed that the edge detection based on the Roberts Cross operator performs consistently well across multiple face datasets, in which the face images have been taken under a variety of conditions. We have proposed a novel scheme using hashing, for extra verification, in order to harden the security of the stored biometric templates.