Deep Hashing Based Cancelable Multi-Biometric Template Protection

Abstract

The increasing use of multi-biometric authentication has raised concerns about the security of biometric templates. Many template protection methods based on convolutional neural network have been presented, but most involve a trade-off between authentication accuracy and template security. In this paper, we present a cancelable multi-biometric template protection scheme that combines deep hashing with cancelable distance-preserving encryption (CDPE), which provides high template security without degrading the authentication performance. Specifically, a deep hashing based architecture that minimizes the quantization loss is designed to map face and iris traits to binary codes. Next, CDPE is proposed to generate a protected template given the face binary code and a user-specific key obtained from the iris binary code, which preserves the distance between original templates in the protected domain to ensure authentication performance equivalent to unprotected systems. Digital lockers instead of the key are stored to further enhance the security, which can be unlocked with genuine biometric traits to get the correct key during authentication. Theoretical and experimental results on real face and iris datasets show that our scheme can achieve equal error rate of 0.23% and genuine accept rate of 97.54%, while guaranteeing irreversibility, revocability and unlinkability of protected templates.