FingerCode: a filterbank for fingerprint representation and matching
With the identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on the emerging automatic positive personal identification applications, biometrics-based identification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a significant fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information, available in the fingerprints. The proposed filter-based algorithm uses a bank of Gabor filters to capture both the local and the global details in a fingerprint as a compact 640-byte fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. Our initial results show identification accuracies comparable to the best results of minutiae-based algorithms published in the open literature. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary fingerprint information.