JACIII Vol.14 No.1 pp. 63-68
doi: 10.20965/jaciii.2010.p0063


Pyramidal Structure Algorithm for Fingerprint Classification Based on Artificial Neural Networks

Eugène C. Ezin

Unité de Recherche en Informatique et Sciences Appliquées Institut de Mathématiques et de Sciences Physiques Université d’Abomey-Calavi, Benin

October 29, 2008
May 13, 2009
January 20, 2010
feature extraction, fingerprint, biometrics, signal processing, artificial neural networks

Feature extraction plays a primary role in pattern recognition classification. Many context-based and problem-based algorithms have been proposed providing good performance in high-quality fingerprint imaging but fail when declining with poor-quality fingerprints. The pyramidal algorithm we present in this paper operates on an image matrix layer for extracting features from ink-and-paper fingerprints. The effectiveness of the pyramidal algorithm compared to the consolidation algorithm is demonstrated using a backpropagation neural network experiment to test preprocessed data.

Cite this article as:
Eugène C. Ezin, “Pyramidal Structure Algorithm for Fingerprint Classification Based on Artificial Neural Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.1, pp. 63-68, 2010.
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