Integrating Local and Global Features in Automatic Fingerprint Verification

Authors:
Anna Vallarta Ceguerra;Irena Koprinska
Affiliations:
-;-
Venue:
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Year:
2002

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Abstract

This paper presents a new approach for combining local and global recognition schemes for Automatic Fingerprint Verification (AFV), by using matched local features as the reference axis for generating global features. In our specific implementation, minutia-based and shape-basedtechniques were combined. The first one matches local features (minutiae) by a point-pattern matching algorithm. The second one generates global features (shape signatures) by using the matched minutiae as its frame of reference. Shape signatures are then digitised to form afeature vector describing the fingerprint. Finally, a LVQ neural network was trained to match the fingerprints by using the difference of a pair of feature vectors. The experimental results show that the integrated system significantly outperforms the minutiae-based system in terms ofclassification accuracy and stability. This makes the new approach a promising solution for biometric applications.