Combining feature level and matching score level fusion strategies for multi-biometrics

Multi-biometrics makes a big progress for the subject of biometrics. Multi-biometrics usually obtains a higher accuracy and reliability than single biometrics. Multi-biometrics depends on a fusion strategy to achieve this. The feature level and matching score level fusion seem to be two widely used...

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Bibliographic Details
Published in2011 International Conference on Remote Sensing, Environment and Transportation Engineering pp. 3930 - 3933
Main Authors Ningbo Zhu, Fu Yu, Qinglong Tian
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2011
Subjects
Authentication
Biometrics
Computers
decision level
Educational institutions
Face recognition
Feature extraction
feature level
matching score level
Multi-biometrics
multi-spectral
PCA-based algorithm
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Summary:Multi-biometrics makes a big progress for the subject of biometrics. Multi-biometrics usually obtains a higher accuracy and reliability than single biometrics. Multi-biometrics depends on a fusion strategy to achieve this. The feature level and matching score level fusion seem to be two widely used and very effective fusion strategies. In this paper, we propose to combine a feature level and matching score level fusion strategies to perform personal authentication. The feature level fusion strategy fuses two biometric traits by using a PCA-based algorithm and the matching score level fusion strategy integrates the results for ultimate personal authentication. The experimental results on a multi-spectral palmprint image database show that the proposed method is feasible and effective.
ISBN:9781424491728
142449172X
DOI:10.1109/RSETE.2011.5965178