A neural network approach to off-line signature verification using directional PDF
作者： J.-P. Drouhard , R. Sabourin , M. Godbout
DOI: 10.1016/0031-3203(95)00092-5
关键词: Classifier (UML) 、 Artificial neural network 、 Mathematics 、 Artificial intelligence 、 Pattern recognition 、 Decision rule 、 Margin classifier 、 Shape factor 、 Backpropagation 、 Off line 、 Probability density function
摘要: A neural network approach is proposed to build the first stage of an Automatic Handwritten Signature Verification System. The directional Probability Density Function was used as a global shape factor and its discriminating power enhanced by reducing cardinality via filtering. Various experimental protocols were implement backpropagation (BPN) classifier. comparison, on same database with decision rule, shows that BPN classifier clearly better than threshold compares favourably k-Nearest-Neighbour
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