Handwritten Signature Research Articles

Selection of handwritten signature dynamic indicators for user authentication

Selection and evaluation of handwritten signature indicators in user authentication systems are considered. An important and unresolved issue of information security is the effective identification of the user who accesses confidential information. Traditional password protection has a number of disadvantages. As an alternative to the password system or its addition, user identification by biometric characteristics is considered. An identifier that uses biometric characteristics is inextricably linked to the user and it is almost impossible to use it without authorization. The decision about the truth of the user of biometric authentication systems is made on the basis of comparison of his template with the data entered when trying to authenticate. The template is formed on the basis of studying the selected individual characteristics of the user. To do this, we use the biometric characteristics of users, which reflect the dynamic, behavioral characteristics of the person. It is proposed to use a handwritten signature as a biometric characteristic of the user. A handwritten signature is a socially and legally recognized biometric characteristic used for human authentication. It has a rather complex structure and high detail - all this makes solving the problem of user identification by mathematical methods quite complex and requires high computational costs. Another significant disadvantage is that handwritten authentication systems require the installation of additional specialized equipment, which makes the use of such systems as an ordinary means of authentication very expensive. To solve this problem, a scheme for implementing a computer data protection system against unauthorized access based on handwritten signatures using mobile devices based on the Android operating system as signature input devices is proposed. A method of biometric vector generation for use in dynamic biometric user authentication systems is proposed. The optimal characteristics are investigated and the efficiency of using the proposed form biometric characteristics vector is estimated. Certificates of copyrights registration for software applications developed during the work were received.

Handwritten Signature Identification Based on Wavelet Transform and CPN Network

Handwritten Signature Identification Based on Wavelet Transform and CPN Network

Use of the Shearlet Transform and Transfer Learning in Offline Handwritten Signature Verification and Recognition

Despite the growing growth of technology, handwritten signature has been selected as the first option between biometrics by users. In this paper, a new methodology for offline handwritten signature verification and recognition based on the Shearlet transform and transfer learning is proposed. Since, a large percentage of handwritten signatures are composed of curves and the performance of a signature verification/recognition system is directly related to the edge structures, subbands of shearlet transform of signature images are good candidates for input information to the system. Furthermore, by using transfer learning of some pre-trained models, appropriate features would be extracted. In this study, four pre-trained models have been used: SigNet and SigNet-F (trained on offline signature datasets), VGG16 and VGG19 (trained on ImageNet dataset). Experiments have been conducted using three datasets: UTSig, FUM-PHSD and MCYT-75. Obtained experimental results, in comparison with the literature, verify the effectiveness of the presented method in both signature verification and signature recognition.

SPHN - Development and Usability Testing of an Electronic General Consent Prototype.

According to the Swiss Law on Research in Humans, the reuse of routinely collected genetic and non-genetic data and samples from patients for research purposes requires the consent of patients. Unfortunately, the so far established paper-based processes are intrinsically linked to the hospital admission process, labour intensive and not yielding the targeted return rates. Therefore, the overall goal of the presented SPHN project is to increase patient reach by providing hospitals with a patient-centric, user-friendly and admission-independent electronic general consent pathway. As part of the project, feasibility of different digital pathways was evaluated in a usability testing. Based on a nationwide harmonised template, a mobile centric progressive web application was developed by the Department of Clinical Research Basel. Usability of the application and according user journeys were evaluated at all partner hospitals. Two options of giving consent were explored using 1) patients' smartphones without any involvement of hospital personnel and 2) a hospital device (tablet) with explicit confirmation of patient identity by hospital personnel. Participant signatures were captured as a picture of a handwritten signature on paper taken with the camera of the smartphone or tablet. Usability issues and feedback of participants were documented directly after usability testing. In total, 122 users agreed to participate in the usability testing using a tablet or smartphone. The general consent request workflow on the smartphone or tablet was regarded as user friendly and easy to navigate by 96% of all participants. However, capturing a picture of a handwritten signature resulted in usability issues in multiple cases, i.e. due to missing pen or paper. Usability testing of our prototype application showed a broad acceptance of participants regarding the use of mobile electronic devices to give general consent. Therefore, we believe that easy-to-use digital general consent processes provide effective means to increase the patient pool for health-related research. Further discussions with legislative bodies are required to find patient centric, feasible and legally acceptable solutions in the specific case of electronic general consent for the near future.

Specimen Authentication System Using Pixel-Based Algorithm

Specimen Authentication is the process used to recognize an individual's handwritten signature. The pre-process of verifying a signature is Pixel-based. There is a need for an automatic verification system for a signature since the signature has been a means of a person's identification for ages. Offline systems work on the scanned image of a signature. The main objective of the study was to design and develop a Specimen Authentication System to examine signatures. In this study, the researchers verify the off-line signatures by taking a boundary of the entire signature and make the pixel comparison. Signature is acquired using a scanner. The detection process is done after the data acquisition and pre-processing. Pre-processing includes noise removal, grey-scale, manipulation, edge detection. Experimental results show that 50% of the accurate matching with the existing one. There is an important distinction between simple signature Comparisons and dynamic signature verification. Both can be computerized, but a Simple comparison only takes into account what the signature looks like. Dynamic signature verification takes into account how the signature was made. With dynamic signature verification, it is not the shape or looks of the meaningful signature; it is the change in speed, pressure, and timing that occur during the act of signing. Consistency created by natural motion and practice over time creates a recognizable pattern that makes the hand-written signature a natural for biometric identification.

ПОДХОДЫ К ОНЛАЙН ВЕРИФИКАЦИИ СОБСТВЕННОРУЧНОЙ ПОДПИСИ

ПОДХОДЫ К ОНЛАЙН ВЕРИФИКАЦИИ СОБСТВЕННОРУЧНОЙ ПОДПИСИ

An Offline Signature Verification and Forgery Detection Method Based on a Single Known Sample and an Explainable Deep Learning Approach

Signature verification is one of the biometric techniques frequently used for personal identification. In many commercial scenarios, such as bank check payment, the signature verification process is based on human examination of a single known sample. Although there is extensive research on automatic signature verification, yet few attempts have been made to perform the verification based on a single reference sample. In this paper, we propose an off-line handwritten signature verification method based on an explainable deep learning method (deep convolutional neural network, DCNN) and unique local feature extraction approach. We use the open-source dataset, Document Analysis and Recognition (ICDAR) 2011 SigComp, to train our system and verify a questioned signature as genuine or a forgery. All samples used in our testing process are collected from a new author whose signatures are not present in the training or other stages. From the experimental results, we get the accuracy between 94.37% and 99.96%, false rejection rate (FRR) between 5.88% and 0%, false acceptance rate (FAR) between 0.22% and 5.34% in our testing dataset.

The hybrid smart contract agreement challenge to European electronic signature regulation

Abstract The article investigates the smart contract used in the Initial Coin Offering (ICO) process and its qualification under the typology of form of contract and the EU electronic signature regulation eIDAS.1 ICOs took the globe by storm in 2017 and created a lot of turmoil among the regulators due to a new form of raising funds globally. In addition to their effect on the capital market, another phenomenon that saw a rise in popularity was the smart contract. The smart contract is usually built into the ICO process as a protocol to execute the issue of a token. The article suggests that the contract in the ICO process does not only refer to the smart/contract code in silos but should be considered in the larger context as the so-called hybrid smart contract agreement with the smart contract protocol being merely the execution motor for the issuance of the token. The article qualifies the contract concluded during the ICO process under the general typology of forms of contract with the aim to identify whether the hybrid smart/contract agreement is in electronic form of contract. Some states in the USA and a few Member States in the EU have also introduced smart contract-specific regulation clearly stating that smart contracts are contracts either in electronic or written form. Still, as this is not prevalent in the EU law, the principle of functional equivalence is used to assess whether the signature on a smart contract used in an ICO process is functionally equivalent to the qualified electronic signature under eIDAS. The existence of a qualified electronic signature allows the contract to be qualified as a contract in electronic form equivalent to a paper-form agreement with hand-written signatures. Furthermore, the article investigates whether the centralized trust system of the eIDAS creates an infrastructural bias against the source of trust in case of distributed ledger technology that in itself could be non-compliant with the principle of technology neutrality.

Handwritten signature verification using shallow convolutional neural network

Handwritten signatures are an undeniable and unique way to prove the identity of persons. Owing to the simplicity and uniqueness, it finds an essential place in the area of behavioral biometric. Signatures are the most widely accepted biometric trait by law enforcement agencies/personnel for verification purposes, especially in financial institutions, legal transactions, etc. and hence secured authentication becomes imperative. In the era of a digital age, numerous transactions are taking place, where handwritten signature verification is required by the agencies, e.g., banks, etc. In such scenarios, the process of signature verification, besides being accurate and secure, should be very fast, i.e., the real-time verification can be done. In this paper, we have proposed a convolutional neural network-based language-independent shallow architecture (sCNN(Shallow Convolutional Neural Network)) for signature verification. The proposed architecture is very simple but extremely efficient in terms of accuracy. A custom shallow convolution neural network is used to automatically learn the features of signature from the provided training data. Another contribution of the research work, which is the handwritten signature data collection for 137 subjects and 467 subjects, which are named as CVBLSig-V1 and CVBLSig-V2 respectively, has been reported in this paper. The performance of the proposed architecture has been evaluated on publicly available datasets, i.e., MCYT-75, MCYT-100, and GPDS, as well as CVBLSig-V1 and CVBLSig-V2. The performance was also compared with state of the art reported methods and shown improved, while considering the accuracy and equal error rate (EER) as performance metrics.

Offline signature verification system: a novel technique of fusion of GLCM and geometric features using SVM

In the area of digital biometric systems, the handwritten signature plays a key role in the authentication of a person based on their original samples. In offline signature verification (OSV), several problems exist that are challenging for verification of authentic or forgery signature by the digital system. Correct signature verification improves the security of people, systems, and services. It is applied to uniquely identify an individual based on the motion of pen as up and down, signature speed, and shape of a loop. In this work, the multi-level features fusion and optimal features selection based automatic technique is proposed for OSV. For this purpose, twenty-two Gray Level Co-occurrences Matrix (GLCM) and eight geometric features are calculated from pre-processing signature samples. These features are fused by a new parallel approach which is based on a high-priority index feature (HPFI). A skewness-kurtosis based features selection approach is also proposed name skewness-kurtosis controlled PCA (SKcPCA) and selects the optimal features for final classification into forged and genuine signatures. MCYT, GPDS synthetic, and CEDAR datasets are utilized for validation of the proposed system and show enhancement in terms of Far and FRR as compared to existing methods.

A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification

High number of writers, small number of training samples per writer with high intra-class variability and heavily imbalanced class distributions are among the challenges and difficulties of the offline Handwritten Signature Verification (HSV) problem. A good alternative to tackle these issues is to use a writer-independent (WI) framework. In WI systems, a single model is trained to perform signature verification for all writers from a dissimilarity space generated by the dichotomy transformation. Among the advantages of this framework is its scalability to deal with some of these challenges and its ease in managing new writers, and hence of being used in a transfer learning context. In this work, we present a white-box analysis of this approach highlighting how it handles the challenges, the dynamic selection of references through fusion function, and its application for transfer learning. All the analyses are carried out at the instance level using the instance hardness (IH) measure. The experimental results show that, using the IH analysis, we were able to characterize “good” and “bad” quality skilled forgeries as well as the frontier region between positive and negative samples. This enables futures investigations on methods for improving discrimination between genuine signatures and skilled forgeries by considering these characterizations.

Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures.

We aim at enhancing personal identity security on mobile touch-screen sensors by augmenting handwritten signatures with specific additional information at the enrollment phase. Our former works on several available and private data sets acquired on different sensors demonstrated that there are different categories of signatures that emerge automatically with clustering techniques, based on an entropy-based data quality measure. The behavior of such categories is totally different when confronted to automatic verification systems in terms of vulnerability to attacks. In this paper, we propose a novel and original strategy to reinforce identity security by enhancing signature resistance to attacks, assessed per signature category, both in terms of data quality and verification performance. This strategy operates upstream from the verification system, at the sensor level, by enriching the information content of signatures with personal handwritten inputs of different types. We study this strategy on different signature types of 74 users, acquired in uncontrolled mobile conditions on a largely deployed mobile touch-screen sensor. Our analysis per writer category revealed that adding alphanumeric (date) and handwriting (place) information to the usual signature is the most powerful augmented signature type in terms of verification performance. The relative improvement for all user categories is of at least 93% compared to the usual signature.

Bookplate design by Stanisław Wyspiański for Lucjan Rydel, kept at the Jagiellonian Library

The subject of the article is Stanisław Wyspiański’s bookplate design for Lucjan Rydel, drawn by watercolour on grey paper (23,8 x 16,9 cm). As a standing rectangle, it shows symmetrically arranged twigs with flower buds, which resemble magnolias and freesias. e signs that appear next to the twigs, which are painted emerald green and light violet, are Z Księgozbioru (“From the book collection”) as well as Dr (?) LRydla [“Dr (?) LRydel’s”], the latter being the facsimile of a signature. It can be assumed that this drawing was Wyspiański’s present for his friend, gifted sometime in the years 1894–1899. Rydel never made a decision to have it printed, which undoubtedly contributed to its status of being unknown among experts and absent from the topic literature, either printed, handwritten or epistolary. The piece is nevertheless particularly important for the history of Polish bookplates. is seemingly ordinary drawing appears revolutionary when one considers that it shows an emblem shield deprived of the actual emblem, lacks an epic theme, does not have any text, and features art nouveau embellishments as well as a handwritten signature of the owner. It can be compared to the works created already in the first decade of the twentieth century by artists such as Jan Bukowski, Antoni Procajłowicz and Kazimierz Sichulski.

Offline Handwritten Signature Recognition Based on SIFT and SURF Features Using SVMs

Biometric recognition for human identification plays a key role in the rapid development of computer vision and pattern recognition research areas. The biometrics, refers to the automatic identification of a person based behavioral characteristics, physiological properties or traits. Signature recognition is one such human identification method and can be performed either in offline or online mode. This paper proposed an offline handwritten signature recognition which is based on image processing technique, scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) features and support vector machines (SVMs). The handwritten signature images were then recognized through the proposed method that involves identification of regions of interest and representation of those regions as SIFT or SURF, construction of codebooks and the multi-class classification of the feature histograms using support vector machines (SVMs). Experiments have been carried out with our dataset of 1600 samples and a recognition rate in excess of 95% was obtained over the ten-fold cross validations.

A Recognition System for Subjects' Signature Using the Spatial Distribution of Signature Body

This investigation proposed an identification system of offline signature by utilizing rotation compensation depending on the features that were saved in the database. The proposed system contains five principle stages, they are: (1) data acquisition, (2) signature data file loading, (3) signature preprocessing, (4) feature extraction, and (5) feature matching. The feature extraction includes determination of the center point coordinates, and the angle for rotation compensation (θ), implementation of rotation compensation, determination of discriminating features and statistical condition. During this work seven essential collections of features are utilized to acquire the characteristics: (i) density (D), (ii) average (A), (iii) standard deviation (S) and integrated between them (iv) density and average (DA), (v) density and standard deviation (DS), (vi) average and standard deviation (AS), and finally (vii) density with average and standard deviation (DAS). The determined values of features are assembled in a feature vector used to distinguish signatures belonging to different persons. The utilized two Euclidean distance measures for matching stage are: (i) normalized mean absolute distance (nMAD) (ii) normalized mean squared distance (nMSD). The suggested system is tested by a public dataset collect from 612 images of handwritten signatures. The best recognition rate (i.e., 98.9%) is achieved in the proposed system using number of blocks (21×21) in density feature set. With the same number of blocks (i.e., 21×21) the maximum verification accuracy obtained is (100%).

ПРАКТИКА ИСПОЛЬЗОВАНИЯ ЭЛЕКТРОННОЙ ЦИФРОВОЙ ПОДПИСИ В ПРЕДПРИНИМАТЕЛЬСКОЙ ДЕЯТЕЛЬНОСТИ

The article deals with the problems of the practice of obtaining changes and using an electronic signature (EP) by entrepreneurs in their activities, specifies the normative legal acts regulating the procedure for obtaining an electronic signature by relevant persons and submitting the necessary documents to the certification centers for obtaining it, specifies the types of EP — simple and enhanced, the procedure for using a simple unqualified and enhanced qualified signature. The concept of an electronic signature is defined in accordance with the law. The information contained in the item instance is described: the signature of the person who signed the document, the date and time of signing, its authority and relation to the data being signed, technical information, information for additional signature verification mechanisms, comments, files, a graphic image of the handwritten signature, and other functionally required data. The practice of judicial authorities is analyzed and certain problems of application and use of electronic signatures by legal entities and individuals are identified. We consider the authorities that issue qualified and unqualified certificates of electronic signatures on the territory of the Russian Federation, as well as the procedure for producing certificates for EP keys and their validity period. The reasons for the production of an item instance by certification centers (registration centers) and the package of required documents, depending on the status of the applicant, are set out. The role of trading platforms is indicated. The article considers the need to systematize legislative acts, develop a universal digital signature, and create terminals for sending electronic documents to regulatory authorities.

Online Handwritten Signature Verification and Recognition Based on Dual-Tree Complex Wavelet Packet Transform.

Background:With the increasing advancement of technology, it is necessary to develop more accurate, convenient, and cost-effective security systems. Handwriting signature, as one of the most popular and applicable biometrics, is widely used to register ownership in banking systems, including checks, as well as in administrative and financial applications in everyday life, all over the world. Automatic signature verification and recognition systems, especially in the case of online signatures, are potentially the most powerful and publicly accepted means for personal authentication.Methods:In this article, a novel procedure for online signature verification and recognition has been presented based on Dual-Tree Complex Wavelet Packet Transform (DT-CWPT).Results:In the presented method, three-level decomposition of DT-CWPT has been computed for three time signals of dynamic information including horizontal and vertical positions in addition to the pressure signal. Then, in order to make feature vector corresponding to each signature, log energy entropy measures have been computed for each subband of DT-CWPT decomposition. Finally, to classify the query signature, three classifiers including k-nearest neighbor, support vector machine, and Kolmogorov– Smirnov test have been examined. Experiments have been conducted using three benchmark datasets: SVC2004, MCYT-100, as two Latin online signature datasets, and NDSD as a Persian signature dataset.Conclusion:Obtained favorable experimental results, in comparison with literature, confirm the effectiveness of the presented method in both online signature verification and recognition objects.

Analysis of Various Fractional Order Derivatives Approaches in Assessment of Graphomotor Difficulties

Graphomotor disabilities (GD) are present in up to 30% of school-aged children and are associated with several symptoms in the field of kinematics. Although the basic kinematic features such as velocity, acceleration, and jerk were proved to effectively quantify these symptoms, a recent body of research identified that the theory of fractional calculus can be used to even improve the objective GD assessment. The goal of this study is to extend the current knowledge in this field and explore the abilities of several fractional order derivatives (FD) approximations to estimate the severity of GD in the children population. We enrolled 85 children attending the 3rd and 4th grade of primary school, who performed a combined loop task on a digitizing tablet. Their performance was rated by psychologists and the online handwriting signals were parametrised by kinematic features utilising three FD approximations: Grunwald-Letnikov’s, Riemann–Liouville’s, and Caputo’s. In this study, we showed the differences across the employed FD approaches for the same kinematic handwriting features and their potential in GD analysis. The results suggest that the Riemann-Liouville’s approximation in the field of quantitative GD analysis outperforms the other ones. Using this approach, we were able to estimate the overall score with a low error of 0.65 points, while the scale range is 4. In fact, the psychologists tend to make the error even higher.

Offline Recognition of Handwritten Signatures Based on the SURF and SVM Algorithms

Offline Recognition of Handwritten Signatures Based on the SURF and SVM Algorithms

Handwritten Signature Verification Using CNN with Data Augmentation

Handwritten Signature Verification Using CNN with Data Augmentation