Compactnet: a lightweight convolutional neural network for one-shot online signature verification

Abstract

This paper proposes a method for the online signature verification task that allows the signature to be verified effectively using a single enrolled signature sample. The method utilizes a neural network with two one-dimensional convolutional neural network (1D-CNN) components to extract the vector representation of an online signature. The first component is a global 1D-CNN with full-length kernels. The second component is the standard 1D-CNN with partial length kernels that have been successfully used in many time-series classification tasks. The network is trained from a set of online signature samples to extract the vector representation of unknown signatures. The experimental results demonstrated that when using a vector representation derived from the proposed network, a single unseen enrolled signature sample achieved an Equal Error Rate (EER) of 4.35% when tested against authentic signatures of other users. This result indicates the effectiveness of the network in accurately distinguishing between genuine signatures and those of different users.