Fingerprint classification using Kohonen topologic map

Self organizing maps are efficient for dimension reduction and data clustering. We propose the use of the Kohonen topologic map for fingerprint pattern classification. The learning process takes into account the large intra-class diversity and the continuum of fingerprint pattern types. After a brie...

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Bibliographic Details
Published inProceedings 2001 International Conference on Image Processing (Cat. No.01CH37205) Vol. 3; pp. 230 - 233 vol.3
Main Authors Bernard, S., Boujemaa, N., Vitale, D., Bricot, C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2001
Subjects
Bifurcation
Clustering algorithms
Fingerprint recognition
Fingers
Neural networks
NIST
Prototypes
Self organizing feature maps
Shape
Spatial databases
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Summary:Self organizing maps are efficient for dimension reduction and data clustering. We propose the use of the Kohonen topologic map for fingerprint pattern classification. The learning process takes into account the large intra-class diversity and the continuum of fingerprint pattern types. After a brief introduction to fingerprint domain-specific knowledge and the expert approach, we present an original and intuitive description of the algorithm. For a classification based on the global shape of the fingerprint, we adopted a suitable feature space. Indeed we obtained 88% correct classification on a database composed of 1600 NIST fingerprints.
ISBN:0780367251
9780780367258
DOI:10.1109/ICIP.2001.958093