SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK

TLDR

The paper presents an algorithm for verifying signatures captured on a touch‑sensitive pad. The algorithm employs a Siamese time‑delay neural network that learns to compare signature pairs, then uses one half of the network to generate a feature vector for a new signature, which is compared to a stored vector and accepted if within a threshold. Laboratory experiments demonstrate the system’s performance.

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.