Comparative study of features for fingerprint indexing

Authors:
Shihua He;Chao Zhang;Pengwei Hao
Affiliations:
Key Laboratory of Machine Perception, Ministry of Education, Peking University, Beijing, China;Key Laboratory of Machine Perception, Ministry of Education, Peking University, Beijing, China;Key Laboratory of Machine Perception, Ministry of Education, Peking University, Beijing, China and Department of Computer Science, Queen Mary University of London, London, UK
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

For current fingerprint indexing schemes, global textures and minutiae structures are usually utilized. To extend the existing methods of feature extraction, we study the three most popular local descriptors, SIFT, SURF and DAISY, for fingerprint indexing and give a comparison of indexing performance for evaluation of these three features on public fingerprint databases. For index construction, the locality-sensitive hashing (LSH) is used to efficiently retrieve similarity queries in a small fraction of the database. Experiments show that SURF and DAISY are applicable for fingerprint indexing as SURF features perform equally well or better than SIFT features while DAISY improves not so significantly.