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6 Conclusion and Future Work
We have shown the application of two types of artificial neural networks for signature
verification. It can be seen (Tab. 1) that classification of genuine and forgery signatures
is reliable if the parameters of networks are trained by a sufficient number of dutifully
prepared training patterns. The achieved FRR and FAR are fully comparable with the
results obtained by standard statistical or structural methods, the wide range testing of
both types of ANNs will be carried out in the future.
In our future work, we plan to focus on the following tasks which could improve
the results of verification process:
– including the new valuable features to the feature vector describing signature,
– optimal setting of the parameters of the input and interface layer of ART-2,
– optimal setting of the thresholds l
t
and Q
t
of SOFM verifier,
– checking the possibility of the application of other neural networks (supervised or
unsupervised learned).
References
1. Mautner, P., Matousek, V., Rohlik, O., Kempf, J.: Signature Verification Using Unsupervised
Learned Neural Networks, Proceeding of the 1st IAPR-TC3 Workshop on Artificial Neural
Network in Pattern Recognition, Florence, Italy, 2003
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Codes for Analog Input Patterns, Applied Optics, No. 26, pp. 4919-4930, 1987
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624-630
11. I-pen producer homepage,
http://www.inductum.com/
12. OTM technology overview
http://www.omtech.com/
13. E-pen documentation
http://www.mediaforte.com.sg/products/graphic/epen/
14. N-Scribe product documentation
http://www.n-scribe.com/
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