Performance Evaluation of Score Level Fusion in Multimodal Biometric Systems

• Main Authors: Kevin Octavius Sentosa, 薛有強
• Other Authors: Shi-Jinn Horng
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/24339197005670279813

碩士 === 國立臺灣科技大學 === 資訊工程系 === 96 === In a multimodal biometric system, the effective fusion method is necessary for combining information from various single modality systems. In this paper we examined the performance of sum rule-based score level fusion and Support Vector Machines (SVM)-based score level fusion. Three biometric characteristics were considered in this study: fingerprint, face, and finger vein. We also proposed a new robust normalization scheme which is derived from min-max normalization scheme. Experiments on four different multimodal databases suggest that integrating the proposed scheme in sum rule-based fusion and SVM-based fusion leads to consistently high accuracy. The performance of simple sum rule preceded by our normalization scheme is comparable to another approach which is based on the estimation of matching scores densities. Comparison between experimental results on sum rule-based fusion and SVM-based fusion reveals that SVM-based fusion could attain better performance compared to sum rule-based fusion, provided that the kernel and its parameters have been carefully selected.