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  • 标题:A Directional Feature with Energy based Offline Signature Verification Network
  • 本地全文:下载
  • 作者:Minal Tomar ; Pratibha Singh
  • 期刊名称:International Journal on Soft Computing
  • 电子版ISSN:2229-7103
  • 出版年度:2011
  • 卷号:2
  • 期号:1
  • 出版社:Academy & Industry Research Collaboration Center (AIRCC)
  • 摘要:Signature used as a biometric is implemented in various systems as well as every signature signed by each person is distinct at the same time. So, it is very important to have a computerized signature verification system. In offline signature verification system dynamic features are not available obviously, but one can use a signature as an image and apply image processing techniques to make an effective offline signature verification system. Author proposes a intelligent network used directional feature and energy density both as inputs to the same network and classifies the signature. Neural network is used as a classifier for this system. The results are compared with both the very basic energy density method and a simple directional feature method of offline signature verification system and this proposed new network is found very effective as compared to the above two methods, specially for less number of training samples, which can be implemented practically.
  • 关键词:Neural Network; Directional Feature; Energy Density; Neuron; Back propagation; FAR;FRR
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