Performance evaluation of score level fusion in multimodal biometric systems

  • Authors:

  • Mingxing He;Shi-Jinn Horng;Pingzhi Fan;Ray-Shine Run;Rong-Jian Chen;Jui-Lin Lai;Muhammad Khurram Khan;Kevin Octavius Sentosa

  • Affiliations:

  • School of Mathematics and Computer Engineering, Xihua University, Chengdu 610039, PR China;School of Mathematics and Computer Engineering, Xihua University, Chengdu 610039, PR China and Department of Computer Science and Information Engineering, National Taiwan University of Science and ...;Institute of Mobile Communications Southwest Jiaotong University, Chengdu, Sichuan 610031, PR China;Department of Electronic Engineering, National United University, Miao-Li 36003, Taiwan;Department of Electronic Engineering, National United University, Miao-Li 36003, Taiwan;Department of Electronic Engineering, National United University, Miao-Li 36003, Taiwan;Center of Excellence in Information Assurance, King Saud University, Kingdom of Saudi Arabia;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan

  • Venue:
  • Pattern Recognition
  • Year:
  • 2010

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Abstract

In a multimodal biometric system, the effective fusion method is necessary for combining information from various single modality systems. In this paper the performance of sum rule-based score level fusion and support vector machines (SVM)-based score level fusion are examined. Three biometric characteristics are considered in this study: fingerprint, face, and finger vein. We also proposed a new robust normalization scheme (Reduction of High-scores Effect normalization) 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-based fusion preceded by our normalization scheme is comparable to another approach, likelihood ratio-based fusion [8] (Nandakumar et al., 2008), 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 the latter could attain better performance than the former, provided that the kernel and its parameters have been carefully selected.