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On Learning to Estimate the Block Directional Image of a Fingerprint Using a Hierarchical Neural Network by Khaled Ahmed Nagaty is a Computer Science article available to read on EtoBox.

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This paper presents a hierarchical neural network architecture for computing fingerprints block directional images. Two separately trained neural networks are connected in series. First, the fingerprint image is divided into 16x16 blocks, each block is submitted to the first network which is a back propagation neural network. It has four counters in its output layer one for each direction to count the main directional codes in each fingerprint block. The output of this network is considered the feature vector for the fingerprint block, which is then submitted to the second network. The second network is a self-organized feature maps neural network uses an unsupervised learning strategy to group the fingerprint blocks into distinct directional classes. In this scheme, there is more than one sub-class for each directional class, an agglomerative hierarchical cluster algorithm for merging two clusters is used to merge two classes if their corresponding distances are below a specified threshold. Results obtained with a real world data set indicate the effectiveness of the proposed architecture.

Who reads On Learning to Estimate the Block Directional Image of a Fingerprint Using a Hierarchical Neural Network?

Author

Khaled Ahmed Nagaty

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0893-6080)

Published

2003

Language

EN

Field

Computer Science (Physical Sciences)