  In this paper we propose a novel fingerprint indexing approach for speeding
up in the fingerprint recognition system. What kind of features are used for
indexing and how to employ the extracted features for searching are crucial for
the fingerprint indexing. In this paper, we select a minutia descriptor, which
has been used to improve the accuracy of the fingerprint matching, as a local
feature for indexing and construct a fixed-length feature vector which will be
used for searching from the minutia descriptors of the fingerprint image using
a clustering. And we propose a fingerprint searching approach that uses the
Euclidean distance between two feature vectors as the similarity between two
indexing features. Our indexing approach has several benefits. It reduces
searching time significantly and is irrespective of the existence of singular
points and robust even though the size of the fingerprint image is small or the
quality is low. And the constructed indexing vector by this approach is
independent of the features which are used for indexing based on the
geometrical relations between the minutiae, like one based on the minutiae
triplets. Thus, the proposed approach could be combined with other indexing
approaches to gain a better indexing performance.
