Information fusion in personal biometric authentication based on the iris pattern

Information fusion in biometrics has received considerable attention. This paper focuses on the application of information fusion techniques in iris recognition. To improve the reliability and accuracy of personal identification based on the iris pattern, this paper proposes the schemes of multialgorithmic fusion and multiinstance fusion. Multialgorithmic fusion integrates the improved phase algorithm and the DCT-based algorithm, and multiinstance fusion combines information from the left iris and the right iris of an individual. Both multialgorithmic fusion and multiinstance fusion are carried out at the matching score level and the support vector machine (SVM)-based fusion rule is utilized to generate fused scores for final decision. The experimental results on the noisy iris database UBIRIS demonstrate that the proposed fusion schemes can perform better than the single recognition systems, and further prove that information fusion techniques are feasible and effective to improve the accuracy and robustness of iris recognition especially under noisy conditions.