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

Visual identification by signature tracking

We propose a new camera-based biometric: visual signature identification. We discuss the importance of the parameterization of the signatures in order to achieve good classification results, independently of variations in the position of the camera with respect to the writing surface. We show that affine arc-length parameterization performs better than conventional time and Euclidean arc-length ones. We find that the system verification performance is better than 4 percent error on skilled forgeries and 1 percent error on random forgeries, and that its recognition performance is better than 1 percent error rate, comparable to the best camera-based biometrics

Latest trends in signatures and signature fraud detection

With the development of the digital society, forensic analysis of signatures, in addition to the traditional analysis of signatures written with a writing medium on a common writing base, has also expanded to the analysis of signatures that are digitally scanned or digitally captured. Digitally scanned signatures represent some authentic signatures that have been scanned and implemented in the disputed document using computer technology and can in no way be considered authentic, especially if the scanning was performed by a person whose name is not signed in the disputed signature. Digitally captured signatures are gradually replacing the traditional way of signing, especially in banking transactions both within the Republic of Croatia and internationally. They become part of the modern forensic analysis of signatures and represent the authentic signatures of a person who made the writing with his own handwriting with a pen on the signature plate. The paper aims to present the latest trends in the development of technologies used for signing and also to present the latest trends in signature forgery. Additionally, in this paper, we present the possibilities of analysing such signatures in potential real situations

Handwritten signature verification by independent component analysis

This study explores a method that learns about the image structure directly from the image ensemble in contrast to other methods where the relevant structure is determined in advance and extracted using hand-engineered techniques. In tasks involving the analysis of image ensembles, important information is often found in the higher-order relationships among the image pixels. Independent Component Analysis (ICA) is a method that learns high-order dependencies found in the input. ICA has been extensively used in several applications but its potential for the unsupervised extraction of features for handwritten signature verification has not been explored. This study investigates the suitability of features extracted from images of handwritten signatures using the unsupervised method of ICA to successfully discriminate between different classes of signatures.peer-reviewe

Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers

We present a new approach for online handwritten signature classification and verification based on descriptors stemming from Information Theory. The proposal uses the Shannon Entropy, the Statistical Complexity, and the Fisher Information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results produced surpass state-of-the-art techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups.Comment: Submitted to PLOS On

Automatic Handwritten Signature Verification System for Australian Passports

We present an automatic handwritten signature verification system to prevent identity fraud by verifying the authenticity of signatures on Australian passports. In this work, fuzzy modeling has been employed for developing a robust recognition system. The knowledge base consists of unique angle features extracted using the box method. These features are fuzzified by an exponential membership function, consisting of two structural parameters which have been devised to track even the minutest variations in a person's signature. The membership functions in turn constitute the weights in the Takagi-Sugeno (TS) model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. The efficacy of the proposed system has been tested on a large database of over 1200 signature images obtained from 40 volunteers achieving a recognition rate of more than 99%

Towards a Bayesian evaluation of features in questioned handwritten signatures

In this work, we propose the construction of a evaluative framework for supporting experts in questioned signature examinations. Through the use of Bayesian networks, we envision to quantify the probative value of well defined measurements performed on questioned signatures, in a way that is both formalised and part of a coherent approach to evaluation. At the current stage, our project is explorative, focusing on the broad range of aspects that relate to comparative signature examinations. The goal is to identify writing features which are both highly discriminant, and easy for forensic examiners to detect. We also seek for a balance between case-specific features and characteristics which can be measured in the vast majority of signatures. Care is also taken at preserving the interpretability at every step of the reasoning process. This paves the way for future work, which will aim at merging the different contributions to a single probabilistic measure of strength of evidence using Bayesian networks