Cancelable multibiometrics using random projection and fuzzy quantization for template protection

Abstract

In recent years, biometrics is most extensively used for people authentication over a range of applications. The growing use of biometrics have raised the issues of security and privacy of the templates stored in the database. Various biometric template protection methods have been presented in the past, but the majority of them require a trade-off between matching efficiency and template security. This paper suggests a hybrid technique of template protection for multibiometric system with improved efficiency and robustness against fraudulent attacks. It works over the fusion of different biometrics, in particular the proposed technique is tested on a multimodal system using face and ECG biometrics. Both biometrics and multimodal templates are processed using domain-specific pre-trained models. The template features are projected in a random subspace using a matrix with standard normally distributed values. It prepares a cancelable template that protects the features of original template. To further enhance the security of the system, the cancelable template is quantized using multi-level random fuzziness technique. Thus, adding a second level of defence against fraudulent attacks. The proposed method reports an optimum accuracy of 99.94% with an equal error rate (EER) off 6 x 10-2.