Cancelable Biometrics with Perfect Secrecy for Correlation-Based Matching

In this paper, we propose a novel method of Cancelable Biometrics for correlation-based matching. The biometric image is transformed by Number Theoretic Transform (Fourier-like transform over a finite field), and then the transformed data is masked with a random filter. By applying a particular kind of masking technique, the correlation between the registered image and the input matching image can be computed in masked domain (i.e,., encrypted domain) without knowing the original images. And we proved theoretically that in our proposed method the masked version does not leak any information of the original image, in other words, our proposed method has perfect secrecy. Additionally, we applied our proposed method to finger-vein pattern verification and experimentally obtained very high verification performance.