Efficient and Privacy-Preserving Fusion Based Multi-Biometric Recognition

Abstract

The advance of artificial intelligence has stimulated the rapid development of multi-biometric recognition that recognizes users based on their multiple biometric features. Since biometric features are private information of users, multi-biometric recognition has to be processed in a privacy-preserving way. Although many approaches have been proposed for privacy-preserving multi-biometric recognition, they have some limitations in security and performance. Aiming at this challenge, we propose a privacy-preserving multi-biometric recognition scheme. In this scheme, we first utilize a neural network to train a face and voiceprint fusion model, which obtains better fusion layer parameters and improves the accuracy of fusion recognition. Then, we design an efficient and privacy-preserving multi-biometric recognition scheme based on the MK-CKKS cryptosystem in the decentralized model, which does not rely on a trusted third party to increase its universality. The scheme can guarante recognition accuracy by utilizing a feature-level fusion mechanism, reaching EERs as low as 0.66%. Security analysis shows that the privacy of biometric information and recognition results can be guaranteed. Experimental results validates the efficiency of our scheme.