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

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. And we have proposed a novel scheme using hashing, for extra verification, in order to harden the security of the stored biometric templates.