Cancelable Biometrics Using Noise Embedding

Abstract

This paper presents a cancelable biometric (CB) scheme for iris recognition system. The CB approaches are roughly classified into two categories depending on whether the method stresses more on non-invertibility or on discriminability. The former is to use non-invertibly transformed data for the recognition instead of the original, so that the impostors cannot retrieve the original biometric information from the stolen data. The latter is to use a salting method that mixes random signals generated by user-specific keys so that the imposters cannot retrieve the original data without the key. The proposed CB can be considered a combination of these methods, which applies a non-invertible transform to the salted data for binary biocode input. We use the reduced random permutation and binary salting (RRP-BS) method as the biometric salting, and use the Hadamard product for enhancing the non-invertibility of salted data. Moreover, we generate several templates for an input, and define non-coherent and coherent matching regions among these templates. We show that salting the non-coherent matching regions is less influential on the overall performance. Specifically, embedding the noise in this region does not affect the performance, while making the data difficult to be inverted to the original.