Abstract

Handwritten Signature Verification (HSV) systems have been introduced to automatically verify the authenticity of a user signature. In offline systems, the handwritten signature (represented as an image) is taken from a scanned document, while in online systems, pen tablets are used to register signature dynamics (e.g., its position, pressure and velocity). In online HSV systems, signatures (including the signature dynamics) may be embedded into digital documents. Unfortunately, during their lifetime documents may be repeatedly printed and scanned (or faxed), and digital to paper conversions may result in loosing the signature dynamics. The main contribution of this work is a new HSV system for document signing and authentication. First, we illustrate how to verify handwritten signatures so that signature dynamics can be processed during verification of every type of document (both paper and digital documents). Secondly, we show how to embed features extracted from handwritten signatures within the documents themselves (by means of 2D barcodes), so that no remote signature database is needed. Thirdly, we propose a method for the verification of signature dynamics which is compatible to a wide range of mobile devices (in terms of computational overhead and verification accuracy) so that no special hardware is needed. We address the trade-off between discrimination capabilities of the system and the storage size of the signature model. Towards this end, we report the results of an experimental evaluation of our system on different handwritten signature datasets.

Highlights

  • B IOMETRIC recognition refers to the automatic identification of a person based on his/her anatomical or behavioral characteristics or traits

  • We illustrate how to verify handwritten signatures so that signature dynamics can be processed during verification of every type of document

  • We show how to embed features extracted from handwritten signatures within the documents themselves, so that no remote signature database is needed

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Summary

INTRODUCTION

B IOMETRIC recognition refers to the automatic identification of a person based on his/her anatomical (e.g., fingerprint, iris) or behavioral (e.g., signature) characteristics or traits. Online HSV systems (such as [4], [5], [6], [7], [8]) address only the third point: they are supported by mobile devices, but cannot verify signatures taken from paper documents and are inherently based on remote database servers. The main challenge here is to be able to store the signature dynamics (into documents), within the limited capacity of barcodes: on the one hand, we need to use a signature model whose size is small, while, on the other hand, we need to increase the capacity of state-of-art-barcodes For this reason, we designed a color barcode denoted as High Capacity Colored 2-Dimensional (HCC2D) code [12], [13], which is well-suited for this framework because of its high data capacity (if compared with state of art barcodes). In order to assess the precision and recall of our HSV system, we conduct an experimental study whose results are reported for different data sets of signatures

A LOGICAL VIEW OF THE HSV SYSTEM
The Document Verification Phase
Signature Registration Phase
Signature Verification Phase
EXPERIMENTATION
Tuning the Thresholds to Enhance Precision and Recall
CONCLUSIONS
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