Abstract:

In recent years,biometric-based authentication has played a vital role in our daily life.The multi-modal authentication method by fusing multiple biometrics to authenticate users can provide a higher security and authentication accuracy than single-modal authentication.However,most of the existing multi-modal authentication schemes adopt fusion strategies with fixed rules and parameters to achieve authentication,which cannot adapt to different authentication scenarios,thus resulting in a sub-optimal authentication performance.To solve the above problems,this paper proposes an Adaptive Particle Swarm Optimization based multi-modal authentication scheme that adaptively fuses multiple biometrics at the score level.First,the proposed scheme determines the security level required for the current authentication scenario according to the context information,and then adaptively selects rules and parameters of the fusion strategy to provide secure authentication and to ensure the best authentication performance of the system.Second,the collected multi-modal biometric data after preprocessing and feature extraction is fused using the selected optimal fusion strategy to achieve authentication.Finally,experimental analyses on the public dataset demonstrate that the proposed scheme is of feasibility and effectiveness by actual data,and can achieve a smaller global error rate than existing schemes under the same authentication security requirements.

Key words: adaptive, multi-modal, authentication, score-level fusion, optimization