Automatic Signature Verification Research Articles

Handwritten Signature Verification Method Using Convolutional Neural Network

Automatic signature verification methods play a significant role in providing a secure and authenticated handwritten signature in many applications, to prevent forgery problems, specifically institutions of finance, and transections of legal papers, etc. There are two types of handwritten signature verification methods: online verification (dynamic) and offline verification (static) methods. Besides, signature verification approaches can be categorized into two styles: writer dependent (WD), and writer independent (WI) styles. Offline signature verification methods demands a high representation features for the signature image. However, lots of studies have been proposed for WI offline signature verification. Yet, there is necessity to improve the overall accuracy measurements. Therefore, a proved solution in this paper is depended on deep learning via convolutional neural network (CNN) for signature verification and optimize the overall accuracy measurements. The introduced model is trained on English signature dataset. For model evaluation, the deployed model is utilized to make predictions on new data of Arabic signature dataset to classify whether the signature is real or forged. The overall obtained accuracy is 95.36\% based on validation dataset.

Enhancing Signature Forgery Detection System Using CNN-SVM

Handwritten signatures are widely used as a means of personal identification and authentication. Many documents like bank cheques and legal transactions require signature verification. But considering a large number of documents, it is a very difficult and time-consuming task. Therefore, ensuring the necessity for a robust automatic signature verification tool that aims to reduce fraud in all related financial transaction sectors. The current visual verification depends mainly on the experience, mood, and working environment of the verifier which ultimately wastes both time and money. Moreover, it is difficult for the eyes of any experts to precisely verify the ratios between lines and angles of a genuine signature to a fraud signature. Therefore, we propose an automatic signature verification technique using the recent advances in image processing and machine learning.

Fuzzified Deep Learning based Forgery Detection of Signatures in the Healthcare Mission Records

In an era subjected to digital solutions, handwritten signatures continue playing a crucial role in identity verification and document authentication. These signatures, a form of bio-metric verification, are unique to every individual, serving as a primitive method for confirming identity and ensuring security of an individual. Signatures, apart from being a means of personal authentication, are often considered a cornerstone in the validation of critical documents and processes, especially within the healthcare sector. In healthcare missions, particularly in the regions that are underdeveloped, hand-written records persist as the primary mode of documentation. The credibility of these handwritten documents hinges on the authenticity of the accompanying signatures, making signature verification a paramount safeguard for the integrity and security of medical information. Nonetheless, traditional offline methods of signature identification can be time-consuming and inefficient, particularly while dealing with a massive volume of documents. This arises the evident need for automated signature verification systems. Our research introduces an innovative signature verification system which synthesizes the strengths of fuzzy logic and CNN (Convolutional Neural Networks) to deliver precise and efficient signature verification. Leveraging the capabilities of Fuzzy Logic for feature representation and CNNs for discriminative learning, our proposed hybrid model offers a compelling solution. Through rigorous training, spanning a mere 28 epochs, our hybrid model exhibits remarkable performance by attaining a training accuracy of 91.29% and a test accuracy of 88.47%, underscoring its robust generalization capacity. In an era of evolving security requirements and the persistent relevance of handwritten signatures, our research links the disparity between tradition and modernity.

Signature Verification System Using SSIM In Image Processing

The verification of signatures is an essential function in several domains, including financial, legal, and administrative processes. Thanks to advancements in image processing, automatic signature verification methods have become more popular. Using structural similarities and image analysis, the proposed research offers a novel approach to signature verification. To compare and assess signatures, it uses the SSIM index. The procedure begins with pre-processing the signature pictures to improve their quality and eliminate any artifacts or noise that may have been obtained from Adobe's stock library. Then, the structural similarity between the reference signature and the input signature is calculated. The perceptual resemblance of two images is measured using structure, contrast, and brightness. The goal of the proposed research is to use this measure to record the signature's structural features and spot changes or deviations. The SSIM value that comes out of the comparison is checked against a threshold that has already been set. To validate an input signature, the calculated similarity must be greater than a certain threshold. The document is marked as suspicious or possibly falsified if it does not comply. Experimental results have shown that the method is effective in differentiating between authentic and counterfeit signatures. By doing away with the need for subjective human judgment and physical examination, this technology provides a reliable and unbiased way to authenticate signatures. Increased automation and trust in signature authentication systems are possible because to the proposed method's encouraging results in accurately differentiating genuine signatures from fakes.

Synergy of on-surface and in-air trajectories: Exploratory analysis of forensic online signatures implementing lessons learned from biometrics

With the increased use of digital devices, forensic document examiners (FDEs) encounter increasing number of dynamic or online signatures during their physical examinations. This shift expands the possibility of examinations and creates new challenges for FDEs. As such, FDEs require new examination skills using data science-based analyses with artificial intelligence and machine-learning techniques. In recent years, automated signature verification has gained significant interest in biometric research and could be useful in forensic investigations. However, the use of complex black-box systems inconveniencing FDEs in explaining the rationale behind their final assessment, especially when dealing with limited signature samples and various types of forged signatures. Therefore, a new forensic method is needed to assist FDEs’ analysis. To tackle these challenges and incorporate lessons learned from biometrics into forensics, this study proposes a novel forensic online signature analysis method. The proposed method uses a single-template strategy based on recent scientific findings in biometrics while updating the strategy for forensic use. This strategy creates a mean-template set from known signature samples that serve as a writer’s signature master pattern. Consequently, FDEs can evaluate intra-writer and inter-item variations using the mean-template set and a questioned signature. Furthermore, to take advantage of recent digital devices, we focused on both on-surface and in-air trajectories of online signatures, which could improve the discriminative power because in-air trajectories are invisible for imposters. Finally, we demonstrate the effectiveness and applicability of the proposed method in a forensic scenario using a public forensic online signature dataset.

A Survey : Deep Learning Approaches for Signature Verification

Since a person's signature serves as the primary authentication and authorization method in legal transactions, there is a greater need than ever for effective auto-mated signature verification solutions. The fact that signatures are already recognized as a popular way of identity verification gives signature verification systems a significant edge over other types of technologies. The methods used to solve this problem and a signature verification system can be categorized into two categories: online and offline. An electronic tablet and pen that are connected to a computer are used in the online technique to extract information about a signature and collect dynamic data for verification purposes, such as pressure, velocity, and writing speed. Offline signature verification, on the other hand, employs signature images that have been recorded by a scanner or camera and involve less electronic management. Extracted features taken from the scanned signature image are used in an offline signature verification system. The main contribution of this study is how we can use deep learning approaches/networks for the task of offline signature verification systems.

Synthesis of 3D on-air signatures with the Sigma–Lognormal model

Signature synthesis is a computation technique that generates artificial specimens which can support decision making in automatic signature verification. A lot of work has been dedicated to this subject, which centres on synthesizing dynamic and static two-dimensional handwriting on canvas. This paper proposes a framework to generate synthetic 3D on-air signatures exploiting the lognormality principle, which mimics the complex neuromotor control processes at play as the fingertip moves. Addressing the usual cases involving the development of artificial individuals and duplicated samples, this paper contributes to the synthesis of: (1) the trajectory and velocity of entirely 3D new signatures; (2) kinematic information when only the 3D trajectory of the signature is known, and (3) duplicate samples of 3D real signatures. Validation was conducted by generating synthetic 3D signature databases mimicking real ones and showing that automatic signature verifications of genuine and skilled forgeries report performances similar to those of real and synthetic databases. We also observed that training 3D automatic signature verifiers with duplicates can reduce errors. We further demonstrated that our proposal is also valid for synthesizing 3D air writing and gestures. Finally, a perception test confirmed the human likeness of the generated specimens. The databases generated are publicly available, only for research purposes, at .

Writer independent handwritten signature verification on multi-scripted signatures using hybrid CNN-BiLSTM: A novel approach

Authenticating important documents by identifying individuals using handwritten signatures make signature verification a critical task. Interpersonal similarity and intrapersonal variation of individuals along with high skilled imitation of signature structure make automatic signature verification a challenging task. In such scenarios, a signature verification system should detect the small differences between genuine and forged signatures with high efficacy. This paper proposed a novel approach towards offline signature verification where a hybrid deep learning network, consisting of a Convolutional Neural Network and a Bidirectional Long Short Term Memory network is used. Signature written with freehand and an imitation of it are almost identical structurally. Deep Neural Network is used to recognize skilled forgery from genuine signatures because of its capability to learn critical details and subtle patterns from the image pixels. A Convolutional Neural Network is trained, and the trained network is then used to extract diverse features of the signature images. The generated feature vectors are then used for classification using Bidirectional Long Short Term Memory. The hybrid deep learning network classifies the input signature as skilled forgery or genuine with high accuracy. For this work, state-of-the-art datasets, such as, GPDS-300, GPDS-Bengali, GPDS-Devanagari, CEDAR, BHSig260-Bengali, BHSig260-Hindi, and a local dataset, Meitei Mayek signature are used. In order to verify the robustness of the system, different multi-scripted offline signatures belonging to multi-lingual Indian society, are used for evaluation. The experimental results determined from multi-scripted signatures, exhibit that the proposed system is found comparable to many state-of-the-art systems and in some specific cases, it out performs some of the existing systems.

Offline signature verification using deep neural network with application to computer vision

Biometric technologies, such as handwritten signature verification, are extremely useful for identifying individuals inside an organization or finance department. The improvement of picture categorization using deep learning (DL) neural networks has offered an opportunity to exhibit computer vision in contemporary research applications by applying image processing approaches. Manual signature verification is inefficient, error-prone, time-consuming, and inconvenient; therefore, it is critical to create an automatic signature verification recognition system. This research offers an automatic recognition method based on DL that makes use of the Grupo de Procesado Digital de Seales. The biggest publicly accessible handwritten signature dataset, the synthetic signature dataset, was used to classify the signatures of 100 people, each of whom possessed 24 genuine signatures and 30 forged signatures. An inception V3 transfer learning (TL) model is proposed by hyper-tuning different layers from the middle of its architecture and this model is fine-tuned by adding layers, such as flatten, dense (1024), dropout (0.5), and dense (1). The suggested model was tested against six well-known pre-trained TL convolutional neural network models: VGG 16, VGG 19, ResNet 50, ResNet 101, MobileNet, and EfficientNet. The suggested model surpasses the pre-trained models. Precision, sensitivity, and F1-score are likewise outperformed by the model, with the values of 88%, 88%, and 87%, respectively. The accuracy of the pre-trained models was evaluated as 80%, 81%, 77%, 73%, 71%, and 74%, respectively. The suggested fine-tuned inception V3 gives the highest accurate classifications, distinguishing between genuine and forged signatures with an accuracy of 88%. This study will aid researchers in developing more effective CNN-based models for offline signature verification with application to computer vision.

Problems of forensic identification of handwriting in forensic examination

The handwritten signature as a characteristic of authentication has been legally and publicly recognised for centuries and has been used in the forensic field for many decades to identify an author. Approaches to biometric user authentication developed in recent years are also based in part on handwriting characteristics, such as automatic signature verification. This article systematically identifies handwriting features published in forensics and analyses which of these properties can be adapted as biometrics to verify the user. A user verification procedure based on a set of 14 forensic characteristics is presented, which is integrated into the biometric verification procedure. The results of the verification of these forensic features are compared in detailed tests with the features of non-forensic data, and it is shown that significant improvements in false detection rates can be achieved by including forensics. For hundreds of years, handwritten signatures have been legally and socially recognised as authentication. The reason for this is the uniqueness of human handwriting. Although a forger with some practice may visually imitate another person’s text or signature, the typical traces resulting from the (studied) behaviour of a victim of forgery are difficult to copy. To check the record, the pressure force of all overlay points with a pen at the beginning or inside the word, written letters on the weight are used. In addition, the way a writer unconsciously or even consciously connects words, letters, or parts of letters to each other is a specific trait for that person. In a forensic examination, experts examine these and other features to prove or disprove the authenticity of signatures or documents. In the field of forensic science, there are many procedures offered based on physical handwriting samples to determine a writer

Intrapersonal Parameter Optimization for Offline Handwritten Signature Augmentation

Usually, in a real-world scenario, few signature samples are available to train an automatic signature verification system (ASVS). However, such systems do indeed need a lot of signatures to achieve an acceptable performance. Neuromotor signature duplication methods and feature space augmentation methods may be used to meet the need for an increase in the number of samples. Such techniques manually or empirically define a set of parameters to introduce a degree of writer variability. Therefore, in the present study, a method to automatically model the most common writer variability traits is proposed. The method is used to generate offline signatures in the image and the feature space and train an ASVS. We also introduce an alternative approach to evaluate the quality of samples considering their feature vectors. We evaluated the performance of an ASVS with the generated samples using three well-known offline signature datasets: GPDS, MCYT-75, and CEDAR. In GPDS-300, when the SVM classifier was trained using one genuine signature per writer and the duplicates generated in the image space, the Equal Error Rate (EER) decreased from 5.71% to 1.08%. Under the same conditions, the EER decreased to 1.04% using the feature space augmentation technique. We also verified that the model that generates duplicates in the image space reproduces the most common writer variability traits in the three different datasets.

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.

SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-World Verification

An open research problem in automatic signature verification is the skilled forgery attacks. However, the skilled forgeries are very difficult to acquire for representation learning. To tackle this issue, this paper proposes to learn dynamic signature representations through ranking synthesized signatures. First, a neuromotor inspired signature synthesis method is proposed to synthesize signatures with different distortion levels for any template signature. Then, given the templates, we construct a lightweight one-dimensional convolutional network to learn to rank the synthesized samples, and directly optimize the average precision of the ranking to exploit relative and fine-grained signature similarities. Finally, after training, fixed-length representations can be extracted from dynamic signatures of variable lengths for verification. One highlight of our method is that it requires neither skilled nor random forgeries for training, yet it surpasses the state-of-the-art by a large margin on two public benchmarks.

Image Processing Based Signature Duplication and its Verification

Currently, a lot of time is needed for the verification of the signature manually. The need of developing an automated checking system is felt because of signature forgery in various transactions. The dynamic signature is a biometric trait that is used in identification. Automatic signature verification is an application of Image Processing. The aim of the model is to identify the correct signature for reducing fraudulent transactions. It is difficult to have multiple signatures of the same person so the idea is to duplicate a given signature number of times by inducing variations and using verifiers to give a similarity. For duplication of signature, the approach of using the Cognitive Inspired Model is used. The approach for creating human-like signatures can be done by introducing Intra-Component and Inter-Component variability. For verification, Structural Similarity Index (SSIM) and Image Hashing techniques are used.

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.

Writer Independent Manipuri Offline Signature Verification using Transfer Learning Technique of Convolutional Neural Network

Automatic Signature Verification system is used to verify whether a signature is genuine or forged. Forged Signatures are those signatures that a person produced by imitating the signature of another person. Automatic Signature Verification is very important as a person’s handwritten signature is used everywhere to authenticate themselves and there is not very much difference between a genuine signature and the imitation of it, i.e. a forged signature. In this work, signature verification is done using different pre-trained Convolutional Neural Networks (CNNs). Convolutional Neural Network has powerful learning ability, and it can be used to distinguish between a genuine and a forged signature automatically. In this experiment, Manipuri signature dataset was used, the dataset was prepared originally and it contains 729 genuine signatures and 243 forged signatures. Features were extracted from pre-trained networks and classification was done using binary Support Vector Machine (SVM) classifier and the performances of the networks were compared. And according to the experiment we achieved a classification accuracy of 84.7 using VGG19 features, accuracy of 86.8 using VGG16 features and accuracy of 81.9 using Alexnet features.

Investigating the Common Authorship of Signatures by Off-Line Automatic Signature Verification Without the Use of Reference Signatures

In automatic signature verification, questioned specimens are usually compared with reference signatures. In writer-dependent schemes, a number of reference signatures are required to build up the individual signer model while a writer-independent system requires a set of reference signatures from several signers to develop the model of the system. This paper addresses the problem of automatic signature verification when no reference signatures are available. The scenario we explore consists of a set of signatures, which could be signed by the same author or by multiple signers. As such, we discuss three methods which estimate automatically the common authorship of a set of off-line signatures. The first method develops a score similarity matrix, worked out with the assistance of duplicated signatures; the second uses a feature-distance matrix for each pair of signatures; and the last method introduces pre-classification based on the complexity of each signature. Publicly available signatures were used in the experiments, which gave encouraging results. As a baseline for the performance obtained by our approaches, we carried out a visual Turing Test where forensic and non-forensic human volunteers, carrying out the same task, performed less well than the automatic schemes.

A Perspective Analysis of Handwritten Signature Technology

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.

Voice verification and identification using i-vector representation

Abstract. In the area of voice recognition, many methods have been proposed over time. Automatic speaker recognition technology has reached a good level of performance, but still needs to be improved. Signature verification (SV) is one of the most common methods of identity verification in the banking sector, where for security reasons, it is very important to have an accurate method for automatic signature verification (ASV). ASV is usually solved by comparing a test signature with a registration signature(-s) signed by the person whose identity is declared in two ways: online and offline. In this study, a new i-vector based method is proposed for SV online. In the proposed method, a fixed-length vector, called an i-vector, is extracted from each signature, and then this vector is used to create a template. Several methods, such as the nuisance attribute projection and the within-class covariance normalization, are also being investigated to reduce the intra-class variation in the i-vector space. At the stage of evaluation and decision-making, they also propose to apply the support vector machine with two classes. In this article, a new low-dimensional space, depending on the dynamics and the channel, is determined using a simple factor analysis, also known as i-vector. I-vectors have proven to be the most efficient functions for text independent speaker verification in recent studies.

Usage of autoencoders and Siamese networks for online handwritten signature verification

In this paper, we propose a novel writer-independent global feature extraction framework for the task of automatic signature verification which aims to make robust systems for automatically distinguishing negative and positive samples. Our method consists of an autoencoder for modeling the sample space into a fixed length latent space and a Siamese Network for classifying the fixed-length samples obtained from the autoencoder based on the reference samples of a subject as being "Genuine" or "Forged." During our experiments, usage of Attention Mechanism and applying Downsampling significantly improved the accuracy of the proposed framework. We evaluated our proposed framework using SigWiComp2013 Japanese and GPDSsyntheticOnLineOffLineSignature datasets. On the SigWiComp2013 Japanese dataset, we achieved 8.65% EER that means 1.2% relative improvement compared to the best-reported result. Furthermore, on the GPDSsyntheticOnLineOffLineSignature dataset, we achieved average EERs of 0.13%, 0.12%, 0.21% and 0.25% respectively for 150, 300, 1000 and 2000 test subjects which indicates improvement of relative EER on the best-reported result by 95.67%, 95.26%, 92.9% and 91.52% respectively. Apart from the accuracy gain, because of the nature of our proposed framework which is based on neural networks and consequently is as simple as some consecutive matrix multiplications, it has less computational cost than conventional methods such as DTW and could be used concurrently on devices such as GPU, TPU, etc.