Integrating local and global features in automatic fingerprint verification

This paper presents anew approach for combining local and global recognition schemes for Automatic Finger-print Verification (AFV), by using matched local features as the reference axis for generating global features. In our specific implementation, minutia-based and shape-based techniques 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 a feature 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 signiflcantly outperforms the minutiae-based system in terms of classification accuracy and stability. This makes the new approach a promising solution for biometric applications.