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Machine Learning for Signature Verification
Abstract
Signature verification is a common task in forensic document analysis. It's aim is to determine whether a questioned signature
matches known signature samples. From the viewpoint of automating the task it can be viewed as one that involves machine learning
from a population of signatures. There are two types of learning tasks to be accomplished: person-independent (or general)
learning and person-dependent (or special) learning. General learning is from a population of genuine and forged signatures
of several individuals, where the differences between genuines and forgeries across all individuals are learnt. The general
learning model allows a questioned signature to be compared to a single genuine signature. In special learning, a person's
signature is learnt from multiple samples of only that person's signature — where within-person similarities are learnt. When
a sufficient number of samples are available, special learning performs better than general learning (5% higher accuracy).
With special learning, verification accuracy increases with the number of samples. An interactive software implementation
of signature verification involving both the learning and performance phases is described.
... Since ancient times, signature has been the government's most widely used and accepted individual biometric mark [2]. Hand-written signatures are widely used for verifying contract letters, checks, bank applications, and statement documents [3]- [5]. Everybody has a unique signature pattern. ...
... 3) Recall or sensitivity will show the percentage of the model's success in finding information. The formula for calculating the precision value is in equation (5). ...
Signature genuine verifications of offline hand-written signatures are critical for preventing forgery and fraud. With the growth of protecting personal identity and preventing fraud, the demand for an automatic system for signature verification is high. The signature verification system is then studied by many researchers using various methods, especially deep learning-based methods. Hence, deep learning has a problem. Deep learning requires much training time for the data to obtain the best model accuracy result. Therefore, this paper proposed a CNN Batch Normalization, the CNN architectural adaptation model with a normalization batch number added, to obtain a CNN model optimization with high accuracy and less training time for offline hand-written signature verification. We compare CNN with our proposed model in the experiments. The research method in this study is data collection, pre-processing, and testing using our private signature dataset (collected by capturing signature images using a smartphone), which becomes the difficulties of our study because of the different lighting, media, and pen used to sign. Experiment results show that our model ranks first, with a training accuracy of 88.89%, an accuracy validation of 75.93%, and a testing accuracy of 84.84%—also, the result of 2638.63 s for the training time consumed with CPU usage. The model evaluation results show that our model has a smaller EER value; 2.583, with FAR = 0.333 and FRR = 4.833. Although the results of our proposed model are better than basic CNN, it is still low and overfitted. It has to be enhanced by better pre-processing steps using another augmentation method required to improve dataset quality.
... It should be noted that training in an automatic signature verification system may be writer-independent (WI) or writer-dependent (WD). [12] In the first case, WI, training is conducted based on a large population of signature samples related to all persons in the dataset, whereas in the case of WD, training is done based on the signature samples of each person, separately. [12] Although WD approach achieves good results, for each user added to the system, a classifier must be conducted again which increases the complexity and cost of the system. ...
... [12] In the first case, WI, training is conducted based on a large population of signature samples related to all persons in the dataset, whereas in the case of WD, training is done based on the signature samples of each person, separately. [12] Although WD approach achieves good results, for each user added to the system, a classifier must be conducted again which increases the complexity and cost of the system. [13] To reduce the complexity, WI approach attracts more researchers in recent years. ...
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.
... e model of general learning allows a questioned signature to be compared to a single genuine signature. A general classifier is designed using an independent database [54]. (2) Person-dependent-learning: also known as special learning, in this type of learning, the signature of each person is learnt from multiple samples of only that person's signature, where person similarities are learnt. ...
Nowadays, the verification of handwritten signatures has become an effective research field in computer vision as well as machine learning. Signature verification is naturally formulated as a machine-learning task. This task is performed by determining if the signature is genuine or forged. Therefore, it is considered a two‐class classification issue. Since handwritten signatures are widely used in legal documents and financial transactions, it is important for researchers to select an efficient machine-learning technique for verifying these signatures and to avoid forgeries that may cause many losses to customers. So far, great outcomes have been obtained when using machine learning techniques in terms of equal error rates and calculations. This paper presents a comprehensive review of the latest studies and results in the last 10 years in the field of online and offline handwritten signature verification. More than 20 research papers were used to make a comparison between datasets, feature extraction, and classification techniques used in each system, taking into consideration the problems that occur in each. In addition, the general limitations and advantages of machine-learning techniques that are used to classify or extract signature features were summarized in the form of a table. We also present the general steps of the verification system and a list of the most considerable datasets available in online and offline fields.
Forgery of a signature with the aim of deception is a serious crime. Machine learning is often employed to detect real and forged signatures. In this study, we present results which argue that robotic arms and generative models can overcome these systems and mount false-acceptance attacks. Convolutional neural networks and data augmentation strategies are tuned, producing a model of 87.12% accuracy for the verification of 2,640 human signatures. Two approaches are used to successfully attack the model with false-acceptance of forgeries. Robotic arms (Line-us and iDraw) physically copy real signatures on paper, and a conditional Generative Adversarial Network (GAN) is trained to generate signatures based on the binary class of 'genuine' and 'forged'. The 87.12% error margin is overcome by all approaches; prevalence of successful attacks is 32% for iDraw 2.0, 24% for Line-us, and 40% for the GAN. Fine-tuning with examples show that false-acceptance is preventable. We find attack success reduced by 24% for iDraw, 12% for Line-us, and 36% for the GAN. Results show exclusive behaviours between human and robotic forgers, suggesting training wholly on human forgeries can be attacked by robots, thus we argue in favour of fine-tuning systems with robotic forgeries to reduce their prevalence.
Signature fraud around the world is increasing atan alarming rate. Fraud in the signature may harm a personor an organization by false transactions and false documentauthorization, which may lead to an irreversible loss. Thus, thepurpose of this research is to predict forged signature usingmachine learning techniques. To attain the objective, differentstate-of-the-art machine learning models, including Neural Net-work, K-Nearest Neighbors, Support Vector Machine, DecisionTree, and Random Forest Classifier, were developed to classifybetween fraud and real signatures. To enhance the performanceof the Neural Network, the VGG-16 pre-trained model was used.As outcome, the transfer learning based Neural Network modelshowed the highest accuracy-96.7%, followed by Support VectorMachine (81.7%), K-Nearest Neighbors (71.7%), Random Forest(70.0%), and Decision Tree (68.3%).
(PDF) An Efficient Transfer Learning Model for Predicting Forged (Handwritten) Signature. Available from: https://www.researchgate.net/publication/360507780_An_Efficient_Transfer_Learning_Model_for_Predicting_Forged_Handwritten_Signature#fullTextFileContent [accessed Jan 28 2024].
Signature Verification is an essential and challenging task in forensic document analysis. Signature Verification is a process where the authenticity of a given signature is validated; i.e. it is confirmed, after analysis, whether the given signature is forged or not. The objective of the work proposed here is to create a new algorithm that can be used for verification of signatures used in cheques and various legal documents for customer authentication in banking systems. The proposed algorithm is designed using Daubechies complex wavelet transform (DCxWT) which has superior approximate shift invariant and edge representation properties over real valued wavelet transform. This is what motivates the use of DCxWT in the Signature Verification task. For classification of valid and forged signatures we have used Support Vector Machine (SVM) classifier. We have used Mutual Information kernel that is an alternative kernel of the KL divergence kernel in SVM classifier. Proposed method is compared with KL-divergence based kernel and some other standard kernels like Poly, RBF, and Tanh kernel of SVM Classifier. The method we proposed is juxtaposed with different state-of-the-art methods and effectiveness of the algorithm is also measured using different quantitative performance measures.
Signatures are widely used to validate the
authentication of an individual. A robust method is still
awaited that can correctly certify the authenticity of a
signature. The proposed solution provided in this paper is
going to help individuals to distinguish signatures for
determining whether a signature is forged or genuine. In our
system, we aimed to automate the process of signature
verification using Convolutional Neural Networks. Our model
is constructed on top of a pre-trained Convolutional Neural
Network called the VGG-19. We evaluated our model on
widely accredited signature datasets with a multitude of
genuine signature samples sourced from ICDAR[3],
CEDAR[1] and Kaggle[2]; achieving accuracies of 100%, 88%,
and 94.44% respectively. Our analysis shows that our
proposed model can classify the signature if they do not closely
resemble the genuine signature.
We first begin with an artificial intelligence approach to crime scenario modelling once a dead body has been found. We then turn to a panoply of contexts and approaches to the processing of human faces: face recognition methods and tools for identification; foreseeing how aging would affect a face (e.g., of a child who went missing); facial expression recognition; digital image forensics (with doctored photographs); facial reconstruction from skeletal remains; and factors in portraiture analysed in the TIMUR episodic formulae model. Having begun with these two major areas (crime scenario modelling, and face processing), we take a broad view of the forensic disciplines of expert opinion, and the sometimes controversial role of statistics in them. We then consider the contribution to forensic science of anthropology and archaeology, as well as software tools for human anatomy. Next, we turn to forensic geology and techniques from geophysics; scent-detection and electronic noses; forensic palynology and its databases; computing in environmental forensics; and forensic engineering. Two large sections, each internally subdivided into nine units, conclude this chapter: “Individual Identification”, and “Bloodstain Pattern Analysis, and the Use of Software for Determining the Angle of Impact of Blood Drops”. The former begins with a history of identification methods, and continues with DNA evidence, and a controversy among statisticians concerning this; we then discuss human fingerprints, and growing skepticism concerning reliability of identification by fingerprints. We then turn to computational techniques for fingerprint recognition, and having surveyed these, we proceed to describe in detail two such techniques.
This is a chapter about what link analysis and data mining can do for criminal investigation. It is a long and complex chapter, in which a variety of techniques and topics are accommodated. It is divided in two parts, one about methods, and the other one about real-case studies. We begin by discussing social networks and their visualisation, as well as what unites them with or distinguishes them from link analysis (which itself historically arose from the disciplinary context of ergonomics). Having considered applications of link analysis to criminal investigation, we turn to crime risk assessment, to geographic information systems for mapping crimes, to detection, and then to multiagent architectures and their application to policing. We then turn to the challenge of handling a disparate mass of data, and introduce the reader to data warehousing, XML, ontologies, legal ontologies, and financial fraud ontology. A section about automated summarisation and its application to law is followed by a discussion of text mining and its application to law, and by a section on support vector machines for information retrieval, text classification, and matching. A section follows, about stylometrics, determining authorship, handwriting identification and its automation, and questioned documents evidence. We next discuss classification, clustering, series analysis, and association in knowledge discovery from legal databases; then, inconsistent data; rule induction (including in law); using neural networks in the legal context; fuzzy logic; and genetic algorithms. Before turning to case studies of link analysis and data mining, we take a broad view of digital resources and uncovering perpetration: email mining, computer forensics, and intrusion detection. We consider the Enron email database; the discovery of social coalitions with the SIGHTS text mining system, and recursive data mining. We discuss digital forensics, digital steganography, and intrusion detection (the use of learning techniques, the detection of masquerading, and honeypots for trapping intruders). Case studies include, for example: investigating Internet auction fraud with NetProbe; graph mining for malware detection with Polonium; link analysis with Coplink; a project of the U.S. Federal Defense Financial Accounting Service; information extraction tools for integration with a link analysis tool; the Poznan ontology model for the link analysis of fuel fraud; and fiscal fraud detection with the Pisa SNIPER project.
In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field.
This paper is a follow up to an article published in 1989 by R. Plamondon and G. Lorette on the state of the art in automatic signature verification and writer identification. It summarizes the activity from year 1989 to 1993 in automatic signature verification. For this purpose, we report on the different projects dealing with dynamic, static and neural network approaches. In each section, a brief description of the major investigations is given.
Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered
We developed a system that automatically authenticates offline handwritten signatures using the discrete Radon transform (DRT) and a hidden Markov model (HMM). Given the robustness of our algorithm and the fact that only global features are considered, satisfactory results are obtained. Using a database of 924 signatures from 22 writers, our system achieves an equal error rate (EER) of 18% when only high-quality forgeries (skilled forgeries) are considered and an EER of 4.5% in the case of only casual forgeries. These signatures were originally captured offline. Using another database of 4800 signatures from 51 writers, our system achieves an EER of 12.2% when only skilled forgeries are considered. These signatures were originally captured online and then digitally converted into static signature images. These results compare well with the results of other algorithms that consider only global features.
This paper presents a set of geometric signature features for offline automatic signature verification based on the description of the signature envelope and the interior stroke distribution in polar and Cartesian coordinates. The features have been calculated using 16 bits fixed-point arithmetic and tested with different classifiers, such as hidden Markov models, support vector machines, and Euclidean distance classifier. The experiments have shown promising results in the task of discriminating random and simple forgeries.
In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field.
Introduction: The World of Documents The History of Questioned Document Examination -In Brief A Handwriting Compendium for Document Examiners The Discrimination of Handwriting The Premises for the Identification of Handwriting The Fundamentals of the Identification Process The Discrimination and Identification of Writing Special Problems in the Discrimination and Identification of Writing The Extrinsical Factors Influencing Handwriting The Intrinsical Influences upon Handwriting The Requirements and the Results The Diagnosis of Writing Identification The Scope of Document Examination The Sources of Document Examiners Science, Scientific Method and Writing Identification Graphology Understanding the Terms-Glossary Epilogue
New machine learning strategies are proposed for person identification which can be used in several biometric modalities such as friction ridges, handwriting, signatures and speech. The biometric or forensic performance task answers the question of whether or not a sample belongs to a known person. Two different learning paradigms are discussed: person-independent (or general learning) and person-dependent (or person-specific learning). In the first paradigm, learning is from a general population of ensemble of pairs, each of which is labelled as being from the same person or from different persons– the learning process determines the range of variations for given persons and between different persons. In the second paradigm the identity of a person is learnt when presented with multiple known samples of that person– where the variation and similarities within a particular person are learnt. The person-specific learning strategy is seen to perform better than general learning (5% higher perfor-mace with signatures). Improvement of person-specific performance with increasing number of samples is also observed.
This study is to examine the metric or non-metric prop-erties of binary vector dissimilarity measures, on which no comprehensive research has been conducted so far. Es-pecially, the triangle-inequality invariance characterizing several dissimilarity measures is revealed. Moreover, an entropy-based measurement is proposed to estimate the dis-criminative capability of a binary vector dissimilarity mea-sure, the effectiveness of the measurement is experimentally justified.
In this paper, a wavelet-based off-line handwritten signature verification system is proposed. The proposed system can automatically identify useful and common features which consistently exist within different signatures of the same person and, based on these features, verify whether a signature is a forgery or not. The system starts with a closed-contour tracing algorithm. The curvature data of the traced closed contours are decomposed into multiresolutional signals using wavelet transforms. Then the zero-crossings corresponding to the curvature data are extracted as features for matching. Moreover, a statistical measurement is devised to decide systematically which closed contours and their associated frequency data of a writer are most stable and discriminating. Based on these data, the optimal threshold value which controls the accuracy of the feature extraction process is calculated. The proposed approach can be applied to both on-line and off-line signature verification systems. Experimental results show that the average success rates for English signatures and Chinese signatures are 92.57% and 93.68%, respectively.
We describe novel methods of feature extraction for recognition of single isolated character images. Our approach is flexible in that the same algorithms can be used, without modification, for feature extraction in a variety of OCR problems. These include handwritten, machine-print, grayscale, binary and low-resolution character recognition. We use the gradient representation as the basis for extraction of low-level, structural and stroke-type features. These algorithms require a few simple arithmetic operations per image pixel which makes them suitable for real-time applications. A description of the algorithms and experiments with several data sets are presented in this paper. Experimental results using artificial neural networks are presented. Our results demonstrate high performance of these features when tested on data sets distinct from the training data.
There are inevitable variations in the signature patterns written by the same person. The variations can occur in the shape or in the relative positions of the characteristic features. In this paper, two methods are proposed to track the variations. Given the set of training signature samples, the first method measures the positional variations of the one-dimensional projection profiles of the signature patterns; and the second method determines the variations in relative stroke positions in the two-dimension signature patterns. The statistics on these variations are determined from the training set. Given a signature to be verified, the positional displacements are determined and the authenticity is decided based on the statistics of the training samples. For the purpose of comparison, two existing methods proposed by other researchers were implemented and tested on the same database. Furthermore, two volunteers were recruited to perform the same verification task. Results show that the proposed system compares favorably with other methods and outperforms the volunteers.
Existing word image retrieval algorithms suffer from either low retrieval precision or high computation complexity. We present an effective and efficient approach for word image matching by using gradient-based binary features. Experiments over a large database of handwritten word images show that the proposed approach consistently outperforms the existing best handwritten word image retrieval algorithm. Dynamic Time Warping (DTW) with profile-based shape features. Not only does the proposed approach have much higher retrieval accuracy, but also it is 893 times faster than DTW.
The product of a unique collaboration among four leading scientists in academic research and industry, Numerical Recipes is a complete text and reference book on scientific computing. In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, the new edition remains the most practical, comprehensive handbook of scientific computing available today.
This paper describes a novel approach for signature verication and identication in an oine environment based on a quasi-multiresolution technique using GSC (Gradient, Structural and Concavity) features for feature extraction. These features when used at the word level, instead of the character level, yield promising results with accuracies as high as 78% and 93% for verication and identication, respectively. This method was successfully employed in our previous theory of individuality of handwriting devel- oped at CEDAR | based on obtaining within and between writer statistical distance distributions. In this paper, exploring signature verication and identication as oine handwriting verication and identication tasks respectively, we depict a mapping from the handwriting domain to the signature domain.
The research here described centers on how a machine can recognize concepts and learn concepts to be recognized. Explanations are found in computer programs that build and manipulate abstract descriptions of scenes such as those children construct from toy blocks. One program uses sample scenes to create models of simple configurations like the three-brick arch. Another uses the resulting models in making identifications. Throughout emphasis is given to the importance of using good descriptions when exploring how machines can come to perceive and understand the visual environment.
In this paper we describe an algorithm that operates on the distances between features in the two related images and delivers a set of correspondences between them. The algorithm maximizes the inner product of two matrices, one of which is the desired 'pairing matrix' and the other a 'proximity matrix' with elements exp (-rij2/2 sigma 2), where rij is the distance between two features, one in each image, and sigma is an adjustable scale parameter. The output of the algorithm may be compared with the movements that people perceive when viewing two images in quick succession, and it is found that an increase in sigma affects the computed correspondences in much the same way as an increase in interstimulus interval alters the perceived displacements. Provided that sigma is not too small the algorithm will recover the feature mappings that result from image translation, expansion or shear deformation--transformations of common occurrence in image sequences--even when the displacements of individual features depart slightly from the general trend.
Motivated by several rulings in United States courts concerning expert testimony in general, and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individual. Handwriting samples of 1,500 individuals, representative of the U.S. population with respect to gender, age, ethnic groups, etc., were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. These attributes, which are a subset of attributes used by forensic document examiners (FDEs), were used to quantitatively establish individuality by using machine learning approaches. Using global attributes of handwriting and very few characters in the writing, the ability to determine the writer with a high degree of confidence was established. The work is a step towards providing scientific support for admitting handwriting evidence in court. The mathematical approach and the resulting software also have the promise of aiding the FDE.
Learning strategies and classification methods for verification of signatures from scanned documents are proposed and evaluated. Learning strategies considered are writer independent- those that learn from a set of signature sample (including forgeries) prior to enrollment of a writer, and writer dependent- those that learn only from a newly enrolled individual. Classification methods considered include two distance based methods (one based on a threshold, which is the standard method of signature verification and biometrics, and the other based on a distance probability distribution), a Nave Bayes (NB) classifier based on pairs of feature bit values and a support vector machine (SVM). Two scenarios are considered for the writer dependent scenario: (i) without forgeries (one-class problem) and (ii) with forgery samples being available (two class problem). The features used to characterize a signature capture local geometry, stroke and topology information in the form of a binary vector. In the one-class scenario distance methods are superior while in the two-class SVM based method outperforms the other methods.
Progress on the problem of signature verification has advanced more rapidly in online applications than offline applications, in part because information which can easily be recorded in online environments, such as pen position and velocity, is lost in static offline data. In offline applications, valuable information which can be used to discriminate between genuine and forged signatures is embedded at the stroke level. We present an approach to segmenting strokes into stylistically meaningful segments and establish a local correspondence between a questioned signature and a reference signature to enable the analysis and comparison of stroke features. Questioned signatures which do not conform to the reference signature are identified as random forgeries. Most simple forgeries can also be identified, as they do not conform to the reference signature's invariant properties such as connections between letters. Since we have access to both local and global information, our approach also shows promise for extension to the identification of skilled forgeries
The decomposition of deformations by principal warps is
demonstrated. The method is extended to deal with curving edges between
landmarks. This formulation is related to other applications of splines
current in computer vision. How they might aid in the extraction of
features for analysis, comparison, and diagnosis of biological and
medical images in indicated
words) is the key for obtaining the discriminating elements of handwriting. While allographs usually inhabit in words and segregation of a word into allographs is more subjective than objective, especially for cursive writing, analysis of handwritten words is a natural and better option. In this study, a handwritten word image is characterized by gradient, structural, and concavity features, and individuality of handwritten words is experimented through writership identification and verification on over 12,000 word images extracted from 3000 handwriting samples of 1000 individuals in U.S.. Experimental results show that handwritten words are very effective in differentiating handwriting and carry more individuality than most handwritten characters (allographs).
This paper is a description of recent advances in off-line signature verification research performed at our laboratory. Related works pertain to structural interpretation of signature images, directional PDF used as a global shape factor, the Extended Shadow Code (ESC) and the fuzzy ESC, a cognitive approach based on the Fuzzy ARTMAP, and shape factors related to visual perception.
Forensic Signature Examination
- S A Slyter
Slyter, S.A.: Forensic Signature Examination. Charles C. Thomas Pub (1995
Questioned Documents
- A Osborn
Off-line signature verification and iden-tification using distance statistics
- M K Kalera
- B Zhang
- S N Srihari
- MK Kalera