Novel approach for multimodal feature fusion to generate cancelable biometric

Biometric systems provide various benefits over traditional pin-based authentication systems. However, the issue of data privacy and theft is of great concern. To resolve these issues, a novel cancelable multimodal biometric system is proposed that combines multiple traits by means of a projection-based approach. The proposed approach generates a cancelable biometric feature that is used to obtain revocable and noninvertible templates. Cancelable features are generated by projecting the feature points onto a random plane obtained using a user-specific key. The point of projection is then transformed into cylindrical coordinates and a combined cancelable feature is obtained. Extensive experiments are performed over 3 chimeric multimodal databases and results reveal high performance. The average DI and EER achieved by the proposed method are 16.63 and 0.004, respectively. Also, the proposed method is successfully analyzed for privacy concerns, namely revocability, non-invertibility, and unlinkability. Moreover, the proposed system demonstrated tolerance against various security attacks like brute force attacks, attacks via record multiplicity, and substitution attacks.

Automated summary

The study introduces a novel cancelable multimodal biometric system that combines multiple traits to generate revocable and noninvertible biometric features for protecting user data privacy and preventing theft. Experiments demonstrate high performance and tolerance against various security attacks.