A New Model of Fingerprint Retrieval Based on Features of Minutiae and Gabor
Fingerprint is the commonly used biometric property in security, commerce and forensic application. One common problem in pattern recognition is lack of samples, only a few fingerprint samples from each individual are available for training a classifier. This paper proposes an approach of fingerprint retrieval based on Bayes classifier by combining the features of Gabor and Minutia and attempted to tackle the problem of insufficient training samples by generating additional samples using spatial modeling. With the expanded training set, the authors are then able to employ a more sophisticated classifier such as a Bayes classifier for recognition.