Computer Controlled Systems: Theory and Design

Learning features for offline handwritten signature verification using deep convolutional neural networks

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

Deep Multitask Metric Learning for Offline Signature Verification

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.

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Offline handwritten signature verification — Literature review

The pivotal role of datasets in signature verification systems motivates researchers to collect signature samples. Distinct characteristics of Persian signature demands for richer and culture-dependent offline signature datasets. This paper introduces a new and public Persian offline signature dataset, UTSig, that consists of 8280 images from 115 classes. Each class has 27 genuine signatures, 3 opposite-hand signatures, and 42 skilled forgeries made by 6 forgers. Compared with the other public datasets, UTSig has more samples, more classes, and more forgers. We considered various variables including signing period, writing instrument, signature box size, and number of observable samples for forgers in the data collection procedure. By careful examination of main characteristics of offline signature datasets, we observe that Persian signatures have fewer numbers of branch points and end points. We propose and evaluate four different training and test setups for UTSig. Results of our experiments show that training genuine samples along with opposite-hand samples and random forgeries can improve the performance in terms of equal error rate and minimum cost of log likelihood ratio.

UTSig: A Persian Offline Signature Dataset

The pivotal role of datasets in signature verification systems motivates researchers to collect signature samples. Distinct characteristics of Persian signature demand for richer and culture-dependent offline signature datasets. This study introduces a new and public Persian offline signature dataset, UTSig (University of Tehran Persian Signature), that consists of 8280 images from 115 classes. Each class has 27 genuine signatures, 3 opposite-hand signatures, and 42 skilled forgeries made by 6 forgers. Compared with the other public datasets, UTSig has more samples, more classes, and more forgers. The authors considered various variables including signing period, writing instrument, signature box size, and number of observable samples for forgers in the data collection procedure. By careful examination of main characteristics of offline signature datasets, they observe that Persian signatures have fewer numbers of branch points and end points. They propose and evaluate four different training and test setups for UTSig. Results of the authors’ experiments show that training genuine samples along with opposite-hand samples and random forgeries can improve the performance in terms of equal error rate and minimum cost of log likelihood ratio.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-bmt.2015.0058

UTSig: A Persian offline signature dataset

The distinctive characteristic of Persian signature implies its own specific feature extraction approaches for verification systems. In this paper, inspired by the cursive nature of Persian signature, and motivated by the opinion of Persian forensic handwritten experts that put emphasis on this factor, we propose a framework for Persian offline signature verification that uses histogram of curvature (HOC) and histogram of oriented gradient (HOG) as features. Calculating curvature for offline signature due to lack of temporal information is not a straightforward task. To this end, we use a discrete curvature estimator that works by normalized gradient and its Jacobian matrix. We investigate the effect of preserving signatures' scale, center of gravity, grid size, grid overlapping, number of bins, and smoothing factor on the accuracy. Support vector machine (SVM) is used to train writer-dependent classifiers on genuine signatures and random forgeries as training samples. Results show competitive performance between HOC and HOG descriptors on UTSig dataset which consists of 8280 samples from 115 classes. Using combination of HOC and HOG descriptors, we achieve promising equal error rate for finding genuine signatures versus skilled forgeries, while false random forgery acceptance is close to zero.

Persian offline signature verification based on curvature and gradient histograms

This paper deals with thermal management of a multicore processor executing multiple stochastic real-time job streams. The main objective is to reduce the chip-wide temperature gradient to decelerate processor aging, and the subordinate goal is to decrease the hotspot temperature. A pair of active and passive cores is dedicated to each stream, which the active one services the corresponding real-time jobs. In order to reduce the chip-wide temperature gradient between cores, the active and passive cores of an individual stream are replaced at appropriate times through job migration. The thermal management of this system is a specific stochastic control problem. Regarding the inter-effects of core temperatures and the stochastic nature of the system, systematic achievement of the objective needs an appropriate method. The control theory of Markov jump linear system (MJLS) has been used to design the desired thermal controller and analytically study its stability. The efficacy of the proposed approach in terms of the thermal management objectives is investigated through simulation experiments.

Kazim Fouladi

Papers

Deep Multitask Metric Learning for Offline Signature Verification

UTSig: A Persian Offline Signature Dataset

UTSig: A Persian offline signature dataset

Persian offline signature verification based on curvature and gradient histograms

Stochastic Thermal Control of a Multicore Real-Time System