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A Classification Approach to Multi-biometric Score Fusion

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3546)

Abstract

The use of biometrics for identity verification of an individual is increasing in many application areas such as border/port entry/exit, access control, civil identification and network security. Multi-biometric systems use more than one biometric of an individual. These systems are known to help in reducing false match and false non-match errors compared to a single biometric device. Several algorithms have been used in literature for combining results of more than one biometric device. In this paper we discuss a novel application of random forest algorithm in combining matching scores of several biometric devices for identity verification of an individual. Application of random forest algorithm is illustrated using matching scores data on three biometric devices: fingerprint, face and hand geometry. To investigate the performance of the random forest algorithm, we conducted experiments on different subsets of the original data set. The results of all the experiments are exceptionally encouraging.

Keywords

Random Forest Class Label Terminal Node Gini Index External Testing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  1. 1.Department of StatisticsWest Virginia UniversityMorgantownUSA
  2. 2.Lane Department of Computer Science and Electrical EngineeringWest Virginia UniversityMorgantownUSA

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