Comparative study of features for fingerprint indexing

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 performanc...

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Bibliographic Details
Published in2009 16th IEEE International Conference on Image Processing (ICIP) pp. 2749 - 2752
Main Authors Shihua He, Chao Zhang, Pengwei Hao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Chaos
Clustering algorithms
Data mining
Detectors
Feature extraction
fingerprint identification
Fingerprint recognition
Indexing
Information retrieval
Nonlinear distortion
Spatial databases
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Summary: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.
ISBN:9781424456536
1424456533
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2009.5414141