Offline Handwritten Signature Verification - Literature Review

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory, Tools and Applications (IPTA 2017

The Value of User-Visible Internet Cryptography

Cryptographic mechanisms are used in a wide range of applications, including email clients, web browsers, document and asset management systems, where typical users are not cryptography experts. A number of empirical studies have demonstrated that explicit, user-visible cryptographic mechanisms are not widely used by non-expert users, and as a result arguments have been made that cryptographic mechanisms need to be better hidden or embedded in end-user processes and tools. Other mechanisms, such as HTTPS, have cryptography built-in and only become visible to the user when a dialogue appears due to a (potential) problem. This paper surveys deployed and potential technologies in use, examines the social and legal context of broad classes of users, and from there, assesses the value and issues for those users

Non-english and non-latin signature verification systems: A survey

Signatures continue to be an important biometric because they remain widely used as a means of personal verification and therefore an automatic verification system is needed. Manual signature-based authentication of a large number of documents is a difficult and time consuming task. Consequently for many years, in the field of protected communication and financial applications, we have observed an explosive growth in biometric personal authentication systems that are closely connected with measurable unique physical characteristics (e.g. hand geometry, iris scan, finger prints or DNA) or behavioural features. Substantial research has been undertaken in the field of signature verification involving English signatures, but to the best of our knowledge, very few works have considered non-English signatures such as Chinese, Japanese, Arabic etc. In order to convey the state-of-the-art in the field to researchers, in this paper we present a survey of non-English and non-Latin signature verification systems

The AutoProof Verifier: Usability by Non-Experts and on Standard Code

Formal verification tools are often developed by experts for experts; as a result, their usability by programmers with little formal methods experience may be severely limited. In this paper, we discuss this general phenomenon with reference to AutoProof: a tool that can verify the full functional correctness of object-oriented software. In particular, we present our experiences of using AutoProof in two contrasting contexts representative of non-expert usage. First, we discuss its usability by students in a graduate course on software verification, who were tasked with verifying implementations of various sorting algorithms. Second, we evaluate its usability in verifying code developed for programming assignments of an undergraduate course. The first scenario represents usability by serious non-experts; the second represents usability on "standard code", developed without full functional verification in mind. We report our experiences and lessons learnt, from which we derive some general suggestions for furthering the development of verification tools with respect to improving their usability.Comment: In Proceedings F-IDE 2015, arXiv:1508.0338

Functional programming languages for verification tools: experiences with ML and Haskell

We compare Haskell with ML as programming languages for verification tools, based on our experience developing TRUTH in Haskell and the Edinburgh Concurrency Workbench (CWB) in ML. We discuss not only technical language features but also the "worlds" of the languages, for example, the availability of tools and libraries

Signature Recognition System for Student Attendance System in UTP

This paper proposes an off-line signature recognition system for student attendance system in Universiti Teknologi PETRONAS (UTP). In current system, attendance sheet is passed across the class and students are required to signed on the paper. Later, lecturers will check on the paper and mark any empty column. However, lecturers always busy and seldom have time to check each signature. Basically, the system has the ability to imitate humans' capability of recognizing signatures. Thus, it could help lecturers in recognizing students' signatures. The system employs artificial neural networks for recognition and training process. This system is developed mainly using Visual Basic 6.0 and involves four basic steps, which are image acquisition, image pre processing, and enrolment and verification process. It has two phases, training and recognition. Both process use artificial neural network. The system was satisfactory in all cases where there were two different signatures to be recognized with False Rejection Rate (FRR) for genuine signature is 4% and False Acceptance Rate (FAR) for forged signature is 28%

e-BioSign Tool: Towards Scientific Assessment of Dynamic Signatures under Forensic Conditions

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. R. Vera-Rodriguez, J. Fierrez, J. Ortega-Garcia, A. Acien and R. Tolosana, "e-BioSign tool: Towards scientific assessment of dynamic signatures under forensic conditions," 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), Arlington, VA, 2015, pp. 1-6. doi: 10.1109/BTAS.2015.7358756This paper presents a new tool specifically designed to carry out dynamic signature forensic analysis and give sci- entific support to forensic handwriting examiners (FHEs). Traditionally FHEs have performed forensic analysis of paper-based signatures for court cases, but with the rapid evolution of the technology, nowadays they are being asked to carry out analysis based on signatures acquired by digi- tizing tablets more and more often. In some cases, an option followed has been to obtain a paper impression of these sig- natures and carry out a traditional analysis, but there are many deficiencies in this approach regarding the low spa- tial resolution of some devices compared to original off-line signatures and also the fact that the dynamic information, which has been proved to be very discriminative by the bio- metric community, is lost and not taken into account at all. The tool we present in this paper allows the FHEs to carry out a forensic analysis taking into account both the tra- ditional off-line information normally used in paper-based signature analysis, and also the dynamic information of the signatures. Additionally, the tool incorporates two impor- tant functionalities, the first is the provision of statistical support to the analysis by including population statistics for genuine and forged signatures for some selected features, and the second is the incorporation of an automatic dy- namic signature matcher, from which a likelihood ratio (LR) can be obtained from the matching comparison between the known and questioned signatures under analysis.This work was supported in part by the Project Bio-Shield (TEC2012-34881), in part by Cecabank e-BioFirma Contract, in part by the BEAT Project (FP7-SEC-284989) and in part by Catedra UAM-Telefonica