Semester
Summer
Date of Graduation
2006
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Lane Department of Computer Science and Electrical Engineering
Committee Chair
Bojan Cukic
Committee Co-Chair
Arun Ross.
Abstract
Multibiometric systems fuse the evidence presented by different biometric sources in order to improve the matching accuracy of a biometric system. In such systems, information fusion can be performed at different levels; however, integration at the matching score level is the most commonly used approach due to the tradeoff between information content and accessibility. This work develops a tool in order to analyze the impact of various normalization schemes on the matching performance of score-level fusion algorithms. The tool permits the systematic evaluation of different fusion rules after employing normalizing and mapping the match scores of different modalities into a common domain. Furthermore, it provides a method to fit various parametric models to the score distribution and analyze the goodness of fit statistic based on the Chi-Squared and Kolmogorov-Smirnov tests. Experimental results on multiple datasets indicate the benefits of normalization, the role of parametric distributions and the variations in matching performance on different databases.
Recommended Citation
Samoska, Nevena, "Evaluation and performance prediction of multimodal biometric systems" (2006). Graduate Theses, Dissertations, and Problem Reports. 1744.
https://researchrepository.wvu.edu/etd/1744