Symmetric sum-based biometric score fusion

Multimodal biometric systems, which combine information from multiple biometric sources, have shown to improve the identity recognition performance by overcoming the weaknesses and some inherent limitations of unimodal systems. A new framework for score level fusion based on symmetric sums (S-sums) has been presented. These S-sums are generated via triangular norms. The proposed framework has been tested on two publicly available benchmark databases. In particular, the authors used two partitions of NIST-BSSR1, i.e. NIST-multimodal database and NIST-fingerprint database. The experimental results show that the proposed method outperforms the existing approaches for the NIST-multimodal database and NIST-fingerprint database.