9th Joint International Conference on Information Sciences (JCIS-06)

Signature recognition using conjugate gradient neural networks

Authors
Jamal Abu Hasna 0
Corresponding Author
Jamal Abu Hasna
0Near East University
Available Online undefined NaN.
DOI
https://doi.org/10.2991/jcis.2006.271How to use a DOI?
Keywords
Signature Verification, MATLAB Software, Conjugate Gradient, Segmentation, Skilled Forgery, and Genuine.
Abstract
SIGNATURE RECOGNITION USING CONJUGATE GRADIENT NEURAL NETWORKS Transforming the input before training yields much lower error, but is more sensitive. Most importantly, we have presented system can vary in security depending on the situation. Uses for such a system range from securing a credit card transaction at the point of sale to user authentication on tablet PCs. We hope that this system will help future research in creating variable security HSV systems as well as systems which can select feature sets which are optimal for a specific user.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
9th Joint International Conference on Information Sciences (JCIS-06)
Publication Date
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ISBN
978-90-78677-01-7
DOI
https://doi.org/10.2991/jcis.2006.271How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jamal Abu Hasna
PY  - NaN/NaN
DA  - NaN/NaN
TI  - Signature recognition using conjugate gradient neural networks
BT  - 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/jcis.2006.271
DO  - https://doi.org/10.2991/jcis.2006.271
ID  - AbuHasnaNaN/NaN
ER  -