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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.

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Nyongesa, H.O., Al-Khayatt, S., Mohamed, S.M. et al. Fast Robust Fingerprint Feature Extraction and Classification. Journal of Intelligent and Robotic Systems 40, 103–112 (2004). https://doi.org/10.1023/B:JINT.0000034344.58449.fd

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  • DOI: https://doi.org/10.1023/B:JINT.0000034344.58449.fd

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