Performance evaluation of fusing protected fingerprint minutiae templates on the decision level

In a biometric authentication system using protected templates, a pseudonymous identifier is the part of a protected template that can be directly compared. Each compared pair of pseudonymous identifiers results in a decision testing whether both identifiers are derived from the same biometric chara...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2012-05, Vol.12 (5), p.5246-5272
Main Authors: Yang, Bian, Busch, Christoph, de Groot, Koen, Xu, Haiyun, Veldhuis, Raymond N J
Format: Article
Language:English
Subjects:
Algorithms
Authentication
Biometrics
Biometry
decision level fusion
Decision Support Techniques
fingerprint
Fingerprints
Interoperability
minutiae template
Performance enhancement
Performance evaluation
pseudonymous identifier
Recognition
template protection
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Summary:In a biometric authentication system using protected templates, a pseudonymous identifier is the part of a protected template that can be directly compared. Each compared pair of pseudonymous identifiers results in a decision testing whether both identifiers are derived from the same biometric characteristic. Compared to an unprotected system, most existing biometric template protection methods cause to a certain extent degradation in biometric performance. Fusion is therefore a promising way to enhance the biometric performance in template-protected biometric systems. Compared to feature level fusion and score level fusion, decision level fusion has not only the least fusion complexity, but also the maximum interoperability across different biometric features, template protection and recognition algorithms, templates formats, and comparison score rules. However, performance improvement via decision level fusion is not obvious. It is influenced by both the dependency and the performance gap among the conducted tests for fusion. We investigate in this paper several fusion scenarios (multi-sample, multi-instance, multi-sensor, multi-algorithm, and their combinations) on the binary decision level, and evaluate their biometric performance and fusion efficiency on a multi-sensor fingerprint database with 71,994 samples.
ISSN:1424-8220
1424-8220
DOI:10.3390/s120505246