One-factor cancelable fingerprint template protection based on feature enhanced hashing

Abstract

In the existing one-factor cancelable biometric template protection scheme, the hashing function used in the transformation of biometrics can’t preserve the original biometric features, which leads to low recognition rate. To make full use of biometric features by replication and extension, but too long feature vectors can cause low computational efficiency. Therefore, a one-factor cancelable fingerprint template protection based on feature enhanced hashing is proposed. Firstly, the extended binary biometric vectors are combined by sliding and extracting window, then converted into decimal system, in order to make full use of biometric features and increase non-invertibility. Secondly, the permutation factor is calculated by the feature enhanced hashing function and the random sequence is reordered, it can embed the information of the original biometric features into the random sequence better. Finally, a cancelable template is generated by reducing the same length of the first and last of reordered random sequence, in this way, some elements can be deleted to improve the computational efficiency and non-invertibility. The experimental results show that the recognition rate of the algorithm is improved on FVC2002 and FVC2004 fingerprint databases, which meets the design standards of cancelable biometric recognition and can defend against security attacks.