Conventional biometric systems use the original biometrics for authentication, which exposes the users' identities to a risk of being compromised. The Randomized Radon Signature (RRS) is a cancellable biometric technique that protects face biometrics during authentication by using Radon transform and random projection. The extracted Radon signatures are generated from the parametric domain of the face and then projected into a random multi-space of a uniform distribution. The generated RRS templates are non-reversible, suitable for image-based statistical face classifiers and reissueable. In this paper the fisherface algorithm is used to conduct a comparison between the original face images and the RRS templates. Results have shown a dramatic 53.37% enhancement in the genuine and impostor distributions separation that leads to a 31.34% reduction in the equal error rate.