Integrating monomodal biometric matchers through logistic regression rank aggregation approach

Biometric system relies on person's behavioral and/or physiological characteristics as an alternative means of person authentication (traditional means being password, smart card, ID etc.). However, biometric system based solely on a single biometric may not always meet security requirements. Thus multibiometric systems are emerging as a trend which helps in overcoming limitations of single biometric solutions, such as when a user does not have a quality sample to present to the system and reduces the ability of the system to be tricked fraudulently. A reliable and successful multibiometric system needs an effective fusion scheme to integrate the information presented by multiple matchers. In this research, we integrate results of three monom.odal biometric matchers (face, ear and iris) with the logistic regression approach of rank level fusion method. In this approach, not only the outcomes of the three mono-modal matchers are considered, but also their effectiveness, based on previous research, are also considered for final rank aggregation. Experiment results indicate that Logistic Regression method outperform Borda count method or plurality voting method. The system can be a contribution to the homeland and border security or other security applications.