Abstract
The signature recognition is a topic of intensive research due to its great importance, among others, in the financial system. However it does not exist yet an enough reliable method for signature recognition and verification, especially in the forgeries detection. This paper presents an off-line signature recognition using features extracted from the off-line signature and an array of five growing cell neural network. The proposed system was evaluated using 950 signatures of 19 different persons. Experimental results show that proposed system achieves a fairly good recognition rate with a relatively low computational complexity
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K. Toscano M., G. Sánchez P., M. Nakano M. y H. Pérez M., “Off-Line Signature Recognition Using Feature Extraction and Multilayer Neural Networks,” To appear in The Journal of Telecommunications and Radio Engineering, 2001.
G. Sánchez P., K. Toscano M., Nakano M. y H. Pérez M., “Growing Cell Neural Network Structure with Backpropagation Learning Algorithm,” To appear in The Journal of Telecommunications and Radio Engineering, 2001.
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© 2001 Springer-Verlag Berlin Heidelberg
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Toscano-Medina, K., Sanchez-Perez, G., Nakano-Miyatake, M., Perez-Meana, H. (2001). A Growing Cell Neural Network Structure for Off-Line Signature Recognition. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_23
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DOI: https://doi.org/10.1007/3-540-45723-2_23
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