&lt;title&gt;Real-time signature verification using neural network algorithms to process optically extracted features&lt;/title&gt;

O'Brien, Ned Francis; Gustafson, Steven C.

doi:10.1117/12.181036

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2008

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“…Table IV shows some of the NN models that have been used recently: Bayesian NNs [30], [351], multilayer perceptrons (MLPs) [7], [15], [17], [126], [167], [345], [350], time-delay NNs [22], [167], ARTMAP NNs [215]- [217], backpropagation neural networks (BPNs) [13], [15], [47], [66]- [68], self-organizing maps [1], [2], and radial basis functions (RBFs) [13], [109], [203], [232], [316]. Fuzzy NN, which combine the advantages of both NNs and fuzzy rule-based systems, has also been considered [102], [270], [353].…”

Section: Classificationmentioning

confidence: 99%

Automatic Signature Verification: The State of the Art

Impedovo

Pirlo

2008

IEEE Trans. Syst., Man, Cybern. C

538

295

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Abstract-In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field.

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