Increasing the robustness of biometric templates for dynamic signature biometric systems

Abstract

El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription Abstract-Due to the high deployment of devices such as smartphones and tablets and their increasing popularity in our society, the use of biometric traits in commercial and banking applications through these novel devices as an easy, quick and reliable way to perform payments is rapidly increasing. The handwritten signature is one of the most socially accepted biometric traits in these sectors due to the fact that it has been used in financial and legal transitions for centuries. In this paper we focus on dynamic signature verification systems. Nowadays, most of the state-of-the-art systems are based on extracting information contained in the X and Y spatial position coordinates of the signing process, which is stored in the biometric templates. However, it is critical to protect this sensible information of the users signatures against possible external attacks that would allow criminals to perform direct attacks to a biometric system or carry out high quality forgeries of the users signatures. Following this problem, the goal of this work is to study the performance of the system in two cases: first, an optimal time functions-based system taking into account the information related to X and Y coordinates and pressure, which is the common practice (i.e. Standard System). Second, we study an extreme case not considering information related to X, Y coordinates and their derivatives on the biometric system (i.e. Secure System), which would be a much more robust system against attacks, as this critical information would not be stored anywhere. The experimental work is carried out using e-BioSign database which makes use of 5 devices in total. The systems considered in this work are based on Dynamic Time Warping (DTW), an elastic measure over the selected time functions. Sequential Forward Features Selection (SFFS) is applied as a reliable way to obtain an optimal time functions vector over a development subset of users of the database. The results obtained over the evaluation subset of users of the database show a similar performance for both Standard and Secure Systems. Therefore, the use of a Secure System can be useful in some applications such as banking in order to avoid the lost of important user information against possible external attacks.

A Perspective Analysis of Handwritten Signature Technology

“…Unfortunately, these approaches result in performance degradation along with reliability problems (i.e., the possibility of revoking a compromised template and defining a new one). Similar results have been observed when simple, noninvertible features have been used [249].…”

“…Due to the high deployment of mobile devices and their increasing popularity in our society, the use of biometric traits, for example, in commercial and banking applications, provides an easy, quick, and reliable way to perform payments through these devices. As was remarked in [249], most state-of-the-art systems are based on extracting information contained in the trajectory coordinates of the signing process, which is stored in a biometric template. Protecting this sensitive information against possible external attack is critical: such attacks, in fact, would allow criminals to perform direct attacks on a biometric system or carry out high-quality forgeries of the users' signatures.…”

Preprocessing and Feature Selection for Improved Sensor Interoperability in Online Biometric Signature Verification

“…Furthermore, it is interesting to study the performance of a dynamic signature verification system applied to security applications, taking into account the extreme case which X and Y coordinates are not used for the system, which would be a much more robust system against attacks [31].…”

Automatic Signature Verification in the Mobile Cloud Scenario: Survey and Way Ahead

“…The most used in the cloudmobile scenario are reported in table I. [6], [8], [9], [11], [15], [32], [49], [56], [57], [91], [93], [94], [97] Model based ANN [106] HMM/GMM [6], [19], [26], [35], [55], [92] SVM [57], [58], [87], [90] Distance-based approaches provide the verification response based on the distance between the test signature and one or more reference signatures. Model-based approaches verify the test signature by estimating fitness on the signature reference model of the user.…”