Pyramidal Structure Algorithm for Fingerprint Classification Based on Artificial Neural Networks | Zendy

Eugène C. Ezin | Zendy

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Pyramidal Structure Algorithm for Fingerprint Classification Based on Artificial Neural Networks

Author(s) -

Eugène C. Ezin

Publication year - 2010

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2010.p0063

Subject(s) - computer science , backpropagation , artificial neural network , artificial intelligence , pattern recognition (psychology) , fingerprint (computing) , algorithm , fingerprint recognition , context (archaeology) , feature extraction , paleontology , biology

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.

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