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In this paper we propose a novel fingerprint indexing approach for speedingup in the fingerprint recognition system. What kind of features are used forindexing and how to employ the extracted features for searching are crucial forthe fingerprint indexing. In this paper, we select a minutia descriptor, whichhas been used to improve the accuracy of the fingerprint matching, as a localfeature for indexing and construct a fixed-length feature vector which will beused for searching from the minutia descriptors of the fingerprint image usinga clustering. And we propose a fingerprint searching approach that uses theEuclidean distance between two feature vectors as the similarity between twoindexing features. Our indexing approach has several benefits. It reducessearching time significantly and is irrespective of the existence of singularpoints and robust even though the size of the fingerprint image is small or thequality is low. And the constructed indexing vector by this approach isindependent of the features which are used for indexing based on thegeometrical relations between the minutiae, like one based on the minutiaetriplets. Thus, the proposed approach could be combined with other indexingapproaches to gain a better indexing performance.

A Fingerprint Indexing Method Based on Minutia Descriptor and Clustering