Multimodal Biometric Authentication Using Choquet Integral and Genetic Algorithm
This work addresses the challenge of information fusion in biometric authentication, but it appears incremental as it applies a known optimization method (genetic algorithm) to an existing fusion tool (Choquet integral).
The authors tackled the problem of selecting fuzzy measures for the Choquet integral in multimodal biometric authentication by proposing a genetic algorithm-based approach, which was validated on synthetic and biometric databases (face, fingerprint, palmprint) and shown to be robust.
The Choquet integral is a tool for the information fusion that is very effective in the case where fuzzy measures associated with it are well chosen. In this paper,we propose a new approach for calculating fuzzy measures associated with the Choquet integral in a context of data fusion in multimodal biometrics. The proposed approach is based on genetic algorithms. It has been validated in two databases: the first base is relative to synthetic scores and the second one is biometrically relating to the face, fingerprintand palmprint. The results achieved attest the robustness of the proposed approach.