Automated Fingerprint Recognition Using the DECOC Classifier

Human fingerprints are rich in details, here called "minutiae". In this paper, a fingerprint recognition system based on a novel application of the classifier DECOC to the minutiae extraction and on an optimised matching algorithm will be presented. To identify the different shapes and types of minutiae, a Data-driven Error Correcting Output Coding (DECOC) has been adopted to work as a classifier. The proposed one has been applied throughout the fingerprint skeleton to locate various minutiae. Extracted minutiae have been used then as identification marks for an automatic fingerprint matching that is based on distance and direction between two minutiae and type of minutiae.

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