Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear witness. Ever since, and over the last 10 years, the application of handwritten signature technology has strongly evolved and much research has focused on the possibility of applying systems based on handwritten signature analysis and processing to a multitude of new fields. After several years of haphazard growth of this research area, it is time to assess its current developments for their applicability in order to draw a structured way forward. This perspective reports a systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject.

https://dl.acm.org/doi/pdf/10.1145/3274658

A Perspective Analysis of Handwritten Signature Technology

Biometric researchers have historically seen signature duplication as a procedure relevant to improving the performance of automatic signature verifiers. Different approaches have been proposed to duplicate dynamic signatures based on the heuristic affine transformation, nonlinear distortion and the kinematic model of the motor system. The literature on static signature duplication is limited and as far as we know based on heuristic affine transforms and does not seem to consider the recent advances in human behavior modeling of neuroscience. This paper tries to fill this gap by proposing a cognitive inspired algorithm to duplicate off-line signatures. The algorithm is based on a set of nonlinear and linear transformations which simulate the human spatial cognitive map and motor system intra-personal variability during the signing process. The duplicator is evaluated by increasing artificially a training sequence and verifying that the performance of four state-of-the-art off-line signature classifiers using two publicly databases have been improved on average as if we had collected three more real signatures.

Generation of Duplicated Off-Line Signature Images for Verification Systems

As an emerging issue, multi-script signature verification is a recent challenge for current Automatic Signature Verification (ASV) systems. Relevant differences are presented in the morphology and lexicon of the signature images written in different scripts, such as used symbols, shape of the signatures, legibility, etc. These peculiarities could reduce the success of ASV systems, especially those which were originally designed for only one kind of script. However, one common feature among scripts in ASV is the fact that the greater the number of signatures that are used for training, the better the expected performance. In this work, we propose a method inspired by observations from the neuromotor equivalence theory to artificially enlarge the signature images used to train a state-of-the-art static signature classifier. Experimental results are obtained by using three static signature datasets derived from completely different scripts: Western, Bengali and Devanagari. Our results suggest that the cognitive-inspired model, which aims to duplicate static signatures, tends toward intra-class variability of signatures written in different scripts; the model's beneficial impact is seen in signature verification tests.

Approaching the intra-class variability in multi-script static signature evaluation

Reduced training sets are major problems typically found on the task of offline signature verification. To increase the number of samples, the use of synthetic signatures can be taken into account. In this work, a new method for the generation of synthetic offline signatures by using dynamic and static (real) ones is presented. The synthesis is here faced under the perspective of supervised training: the learning model is trained to perform the task of online-to-offline signature conversion. The approach is based on a deep convolutional neural network. The main goal is to enlarge offline training dataset in order to improve performance of the offline signature verification systems. For this purpose, a machine-oriented evaluation on the BiosecurID signature dataset is carried out. The use of synthetic samples (in the training phase) generated with the proposed method on a state-of-the-art classification system exhibits performance similar to those obtained using real signatures; moreover, the combination of real and synthetic signatures in the training set is also able to show improvements of the equal error rate.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-bmt.2018.5091

Deep learning approach to generate offline handwritten signatures based on online samples

Handwritten signature datasets are really necessary for the purpose of developing and training automatic signature verification systems. It is desired that all samples in a signature dataset should exhibit both inter-personal and intra-personal variability. A possibility to model this reality seems to be obtained through the synthesis of signatures. In this paper we propose a method based on motor equivalence model theory to generate static Bengali signatures. This theory divides the human action to write mainly into cognitive and motor levels. Due to difference between scripts, we have redesigned our previous synthesizer [1,2], which generates static Western signatures. The experiments assess whether this method can approach the intra and inter-personal variability of the Bengali-100 Static Signature DB from a performance-based validation. The similarities reported in the experimental results proof the ability of the synthesizer to generate signature images in this script.

Multiple Generation of Bengali Static Signatures

Human signatures are widely used for biometric authentication. For automatic online signature verification, rather than storing an image of the completed signature, data are represented in the form of a time series of pen position and status information allowing the extraction of temporal-based features. For visualisation purposes, signature images need to be recreated from time-series data. In this study, the authors investigate the accuracy and verification performance of a series of interpolation methods for recreating a signature image from the time-series data contained in two ISO/IEC data storage formats. The authors experiments investigate dynamic data stored at various sample rates and signature images recreated at differing resolutions. Their study indicates possible best practice in terms of image recreation method, recreated image resolution and temporal sample rate and assesses the effect on the accuracy of reconstructed signature data.

Richard Matthew Guest

Papers

Assessment of methods for image recreation from signature time-series data