Combining Distances through an Auto-Encoder Network to Verify Signatures

In this paper we present a system for off-line signature verification. The paper’s contributions are: i) Five distances were calculated and evaluated over the signature database, they are: furthest, nearest, template, central and ncentral. Also, a normalization procedure is established to turn each distance scale invariant; ii) These distances are combined using the following rules: product, mean, maximum and minimum; iii) The calculated distances can be used as a feature vector to represent a given signature. So, the feature vectors found and their combination were finally used as input vector for an auto-encoder neural network. All the experimental study is done using one-class classification, which demands only the genuine signature to generalize. The proposed approaches achieved very good rates for the signature verification task.

• Type: Conference
• Year: 2008
• Where: SBRN
• Authors: Milena R. P. Souza, Leandro R. Almeida, George D. C. Cavalcanti
• Keywords: Artificial Intelligence, Off-line Signature Verification, SBRN 2008, Signature, Signature Verification