Improved Fingerprint Indexing Based on Extended Triangulation

A simple fingerprint identification scheme compares an input fingerprint with all the fingerprints in the database to find any matching fingerprint. That is, the simple matching method considers all fingerprints in the database as candidates for a given input fingerprint. However, this simple matching method requires a lot of processing time. To reduce the processing time, we can use fingerprint indexing to reduce the size of a candidate set for an input fingerprint. The candidate set is the set of fingerprints most similar to the input fingerprint. Usually, the size of the candidate set is much smaller than the size of the whole fingerprint database. It enables efficient identification by comparing the input fingerprint with only the fingerprints in the candidate set instead of the entire database. In this paper, we analyze the index distribution of the Kavati et al.'s indexing method and propose a new fingerprint index vector which tries to make the index distribution more similar to the uniform distribution. Our new index vector consists of elements that are not highly correlated, which is measured by the Pearson correlation coefficient. Because our indexing method makes the index values widely spread over the index space, it reduces the number of candidates for a given fingerprint in fingerprint identification. Our indexing method shows a higher match rate with a smaller candidate set than the existing triplet-based indexing methods. Especially, our indexing method is up to 6.4 times more accurate than the Kavati et al.'s indexing method. Our result shows that the index distribution significantly affects performance of indexing methods.