Abstract: This paper describes the development of an algorithm for verification of signatures written on a touch-sensitive pad. The signature verification algorithm is based on an artificial neural network. The novel network presented here, called a “Siamese” time delay neural network, consists of two identical networks joined at their output. During training the network learns to measure the similarity between pairs of signatures. When used for verification, only one half of the Siamese network is evaluated. The output of this half network is the feature vector for the input signature. Verification consists of comparing this feature vector with a stored feature vector for the signer. Signatures closer than a chosen threshold to this stored representation are accepted, all other signatures are rejected as forgeries. System performance is illustrated with experiments performed in the laboratory.

@article{bromley-bentz-93,
  author = {Bromley, Jame and Bentz, Jim W. and Bottou, {L\'eon} and Guyon, Isabelle and {Le Cun}, Yann and Moore, C. and {S\"ackinger}, Eduard and Shah, Roopak},
  title = {Signature Verification using a Siamese Time Delay Neural Network},
  journal = {International Journal of Pattern Recognition and Artificial Intelligence},
  volume = {7(4)},
  year = {1993},
  url = {http://leon.bottou.org/papers/bromley-bentz-93},
}