Fast Robust Fingerprint Feature Extraction and Classification

Authors:
H. O. Nyongesa;S. Al-Khayatt;S. M. Mohamed;M. Mahmoud
Affiliations:
Department of Computer Science, University of Botswana, Botswana/ e-mail: nyongesa@mopipi.ub.bw;School of Computing and Management Sciences, Sheffield Hallam University;School of Computing and Management Sciences, Sheffield Hallam University;School of Computing and Management Sciences, Sheffield Hallam University
Venue:
Journal of Intelligent and Robotic Systems
Year:
2004

Citing 8
Cited 3

Fingerprint pattern classification

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Segmentation of fingerprint images using the directional image

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Thinning Methodologies-A Comprehensive Survey

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A Multichannel Approach to Fingerprint Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Classification by Directional Image Partitioning

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Classification with Combinations of Support Vector Machines

AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Fingerprint Classification by Combination of Flat and Structural Approaches

AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
A Structural Approach to Fingerprint Classification

ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276

Application of a new type of singular points in fingerprint classification

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Combining singular points and orientation image information for fingerprint classification

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Fingerprint classification using one-vs-all support vector machines dynamically ordered with naïve Bayes classifiers

Pattern Recognition

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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.