, Volume 40, Issue 1, pp 103-112

Fast Robust Fingerprint Feature Extraction and Classification

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Abstract

Automatic identification of humans based on their fingers is still one of the most reliable identification methods in criminal and forensic applications. Identification by fingerprint involves two processes: fingerprint feature extraction and feature classification. The basic idea of fingerprint feature extraction algorithms proposed is to locate the coarse features of fingerprints called singular-points using directional fields of the fingerprint image. The features are then classified by different types of neural networks. The “five-class” classification problem is addressed on the NIST-4 database of fingerprints. A maximum classification accuracy of 93.75% was achieved and the result shows a performance comparable to previous studies using either coarse features or the finer features called minutiae.