Title: Dynamic signature verification and the human biometric sensor interaction model
Authors: Michael Brockly, Richard Guest, Stephen Elliott, James Scott
Publication date: 2011/10/18
Conference name: Security Technology (ICCST), 2011 IEEE International Carnahan Conference on
Pages: 1-6
Publisher: IEEE
Description:
Abstract-This paper examines the application of the HBSI model to a range of dynamic signature verification capture digitizers. Behavioral biometrics (which considers the process by which a human performs a function) are inherently more complicated to analyze as they may contain a temporal modification as part of a valid capture interaction. In this study, a framework for the development of an HBSI model is outlined, based on two different signature digitizers. Both devices enabled the capture of both temporal data (dynamic) as …

Title: Zero Effort Forgery
Authors: Stephen J. Elliott
Journal: Encyclopedia of Biometrics
Publication date:2009
Description:
An impostor attempt is classed as ”zero-effort”if the individual submits his/her own biometric feature as if he/she were attempting successful verification against his/her own template, but the comparison is made against the template of another user. In the case of dynamic signature verification, an impostor would therefore sign his/her own signature in a zero-effort attempt. In such cases where impostors may easily imitate aspects of the required biometric, a second impostor measure based on ”active impostor attempts”may be required [1]. The …

Dynamic signature verification is a subset of that larger science that includes fingerprint recognition, hand geometry, and voice recognition. Signature verification is primarily behavioral in nature like voice recognition, but has some very unique traits which make it harder to test and evaluate. These challenges include the fact that a signature is learnt, it contains variant measures, it can be changed by the owner of the signature, and that a signer might have several versions of the signature, depending on the intent of the signer. There are a number of digitizers on the market, and making sure that they interoperate is key in developing a DSV solution. This research will examine which variables are interoperable across a number of digitizers. In conjunction with the work done in SC37, a study is underway which examines the “force” variable to understand whether digitizers on the market are calibrated correctly.

The Perception of Signature Strength is another important aspect in understanding the vulnerability of a signature. For example, a forger has access to a number of signatures, some of which they perceive to be difficult as opposed to an “easy” signature. This research attempts to define the strength of a signature, and then to analyze that signature dynamically to estimate the variables that are subjected to weekness. Signatures were redistributed for individuals to rank according to difficulty of forgery and to include reasons on why certain signatures would be difficult to forge. The data was coded to allow for analysis of patterns which indicate what traits make signatures easy to forge vs. traits that make them difficult. Signatures were forged to reveal the quality of the initial perceptions. It is expected that the research will provide evidence of mechanical traits that do in fact indicate ease of forgery to a human subject, as well as those traits that contribute to the difficulty of forgery.

Dynamic Signature Variable Traits in Signature Forgery centers on the fact that the signature may not be verified at that specific moment (unlike the other biometrics), but may be validated at a later date. Furthermore, understanding an impostor distribution is also a challenge in the fact that other biometrics use a zero-effort attempt, “where an impostor uses his or her own biometric sample and claims the identity of a different enrollee” (WG1, 2005). Thus, dynamic signature verification is unique among other biometric authentication methodologies as there is no clear defined way of creating a forgery. This research examines two aspects of a forgery – the first is the perception of the signature to forgery (how easy an individual perceives the signature to be forged), and the second is the amount of knowledge that a forger has about a signature. The dynamic variables of the signature were then examined to establish which statistical variables were susceptible to forgery using forensic tools. For dynamic signature verification, a zero-effort attempt would cause the forger to write their own name instead of that of the target.

Title: Dynamic signature forgery and signature strength perception assessment
Authors: Stephen Elliott, Adam Hunt
Publication date: 2008/6
Journal name: Aerospace and Electronic Systems Magazine, IEEE
Volume: 23
Issue: 6
Pages: 13-18
Publisher: IEEE
Description:
Abstract Dynamic signature verification has many challenges associated with the creation of
the impostor dataset. The literature discusses several ways of determining the impostor
signature provider, but this takes a different approach-that of the opportunistic forger and his
or her relationship to the genuine signature holder. This examines the accuracy with which
an opportunistic forger assesses the various traits of the genuine signature, and whether the
genuine signature holder believes that his or her signature is easy to forge.

This paper discusses the impact of human interaction with biometric devices and its relationship to biometric performance. The authors propose a model outlining the Human-Biometric Sensor Interaction and discuss its necessity through case studies in fingerprint recognition, hand geometry, and dynamic signature verification to further understand the human-sensor interaction issues and underlying problems that they present to the biometric system. Human factors, human-computer interaction and digital human modeling are considered in the context of current and future biometric research and development.

This paper outlines various research projects that have been conducted at Purdue University in the areas of environment, population, and devices. These areas are of interest as biometric technologies are currently being implemented in various business applications. The environmental research is concerned with the performance of a facial recognition algorithm at differing illumination levels. The second study looks at population, which examines differences in image quality with regard to population age.The third study outlines dynamic signature verification and the issues associated with signing on different digitizers.Download article (pdf) 

Identification, Authentication and Privacy
Adam R. Hunt, Stephen J. Elliott, Ph.D.
Dynamic signature verification is a subset of that larger science that includes fingerprint recognition, hand geometry, and voice recognition. Signature verification is primarily behavioral in nature like voice recognition, but has some very unique traits which make it harder to test and evaluate. These challenges include the fact that a signature is learnt, it contains variant measures, it can be changed by the owner of the signature, and that a signer might have several versions of the signature, depending on the intent of the signer.

Title: An Assessment of Dynamic Signature Forgery and Perception of Signature Strength
Authors: Stephen Elliott, Adam Hunt
Publication date: 2006
Conference name: Carnahan Conferences Security Technology, Proceedings 2006 40th Annual IEEE International
Pages: 186-190
Publisher: IEEE
Description:
Abstract- Dynamic signature verification has many challenges associated with the creation of the impostor dataset. The literature discusses several ways of determining the impostor signature provider, but this paper takes a different approach-that of the opportunistic forger and his or her relationship to the genuine signature holder. The paper examines the accuracy with which an opportunistic forger assesses the various traits of the genuine signature, and whether the genuine signature holder believes that his or her signature is …