Automatic Signature Verification System Research Articles

Hidden Signature for DTW Signature Verification in Authorizing Payment Transactions

Traditional use of dynamic time warping for signature verification consists of forming some dissimilarity measure between the signature in question and a set of “template signatures”. In this paper, we propose to replace this set with the hidden signature and use it to calculate the normalized errors of signature under verification. The approach was tested on the MCYT database, using both genuine signatures and skilled forgeries. Moreover, we present the real-world application of the proposed algorithm, namely the complete biometric system for authorizing payment transactions. The authorization is performed directly at a point of sale by the automatic signature verification system based on the hidden signature.

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.

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.

Thai Automatic signature verification System Employing Textural Features

This study focuses on a comprehensive study of Automatic Signature Verification (ASV) for off-line Thai signatures; an investigation was carried out to characterise the challenges in Thai ASV and to baseline the performance of Thai ASV employing baseline features, being Local Binary Pattern, Local Directional Pattern, Local Binary and Directional Patterns combined (LBDP), and the baseline shape/feature-based hidden Markov model. As there was no publicly available Thai signature database found in the literature, the authors have developed and proposed a database considering real-world signatures from Thailand. The authors have also identified their latent challenges and characterised Thai signature-based ASV. The database consists of 5,400 signatures from 100 signers. Thai signatures could be bi-script in nature, considering the fact that a single signature can contain only Thai or Roman characters or contain both Roman and Thai, which poses an interesting challenge for script-independent SV. Therefore, along with the baseline experiments, the investigation on the influence and nature of bi-script ASV was also conducted. From the equal error rates and Bhattacharyya distance, the score achieved in the experiments indicate that the Thai SV scenario is a script-independent problem. The open research area on this subject of research has also been addressed.

Towards the design of an offline signature verifier based on a small number of genuine samples for training

Signature verification has remained one of the most widely accepted modalities to authenticate an individual primarily due to the ease with which signatures can be acquired. Being a behavioral biometric modality, the intra-personal variability in signatures is rather high and extremely unpredictable. This leads to relatively higher error rates as compared to those realized by other biometric traits like iris or fingerprints. To address these issues, this study investigates run-length distribution features for designing an effective offline signature verification system. The objective of this research is to enhance the capabilities of automatic signature verification systems allowing them to work in a realistic fashion by training them using positive specimens (genuine signatures of each person) only without access to any forged samples. Classification is carried out using One-Class Support Vector Machine (OC-SVM) while the evaluations are performed using GPDS960 database, one of the largest offline signature corpus developed till date. Experimental results show the potential of the proposed system for detection of skilled forgeries, especially for the challenging case of a single reference signature in the training set.

Static and Dynamic Synthesis of Bengali and Devanagari Signatures.

Developing an automatic signature verification system is challenging and demands a large number of training samples. This is why synthetic handwriting generation is an emerging topic in document image analysis. Some handwriting synthesizers use the motor equivalence model, the well-established hypothesis from neuroscience, which analyses how a human being accomplishes movement. Specifically, a motor equivalence model divides human actions into two steps: 1) the effector independent step at cognitive level and 2) the effector dependent step at motor level. In fact, recent work reports the successful application to Western scripts of a handwriting synthesizer, based on this theory. This paper aims to adapt this scheme for the generation of synthetic signatures in two Indic scripts, Bengali (Bangla), and Devanagari (Hindi). For this purpose, we use two different online and offline databases for both Bengali and Devanagari signatures. This paper reports an effective synthesizer for static and dynamic signatures written in Devanagari or Bengali scripts. We obtain promising results with artificially generated signatures in terms of appearance and performance when we compare the results with those for real signatures.

Signature authentication based on human intervention: performance and complementarity with automatic systems

This work explores human intervention to improve Automatic Signature Verification (ASV). Significant efforts have been made in order to improve the performance of ASV algorithms over the last decades. This work analyzes how human actions can be used to complement automatic systems. Which actions to take and to what extent those actions can help state-of-the-art ASV systems is the final aim of this research line. The analysis at classification level comprises experiments with responses from 500 people based on crowdsourcing signature authentication tasks. The results allow to establish a human baseline performance and comparison with automatic systems. Intervention at feature extraction level is evaluated using a self-developed tool for the manual annotation of signature attributes inspired in Forensic Document Experts analysis. We analyze the performance of attribute-based human signature authentication and its complementarity with automatic systems. The experiments are carried out over a public database including the two most popular signature authentication scenarios based on both online (dynamic time sequences including position and pressure) and offline (static images) information. The results demonstrate the potential of human interventions at feature extraction level (by manually annotating signature attributes) and encourage to further research in its capabilities to improve the performance of ASV.

Online and Offline Signature Verification: A Combined Approach

Handwritten signature verification is an emerging area. In this paper, an automatic signature verification system has been proposed. This work focuses on both online and offine features of handwritten signatures and aims at combining their results to verify the signature. Signatures are collected for both online and offine. Online data collected is the signing process captured using a webcam and offine data collected are the scanned signatures. Initially both data undergoes appropriate preprocessing steps. Then feature extraction is done where features based on pen tip tracking are used in case of online and gradient and projection based features are used in case of offine method. Later the online and offine method verifies the signature separately and finally their results are combined and the signature is verified using SVM. Paper also compares the results of online, offine and combined approach.

The Influence of Template Ageing on a Dynamic Signature Verification System

This paper addresses template ageing in automatic signature verification systems. Handwritten signatures are a behavioral biometric sensitive to the passage of time. The experiments in this paper utilized a database that contains signature realizations captured in three sessions. The last session was captured seven years after the first one. The results presented in this paper show a potential risk of using an automatic handwriting verification system without including template ageing
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Mobile signature verification: feature robustness and performance comparison

In this study, the effects of using handheld devices on the performance of automatic signature verification systems are studied. The authors compare the discriminative power of global and local signature features between mobile devices and pen tablets, which are the prevalent acquisition device in the research literature. Individual feature discriminant ratios and feature selection techniques are used for comparison. Experiments are conducted on standard signature benchmark databases (BioSecure database) and a state-of-the-art device (Samsung Galaxy Note). Results show a decrease in the feature discriminative power and a higher verification error rate on handheld devices. It is found that one of the main causes of performance degradation on handheld devices is the absence of pen-up trajectory information (i.e. data acquired when the pen tip is not in contact with the writing surface).

OFFLINE SIGNATURE IDENTIFICATION USING HIGH INTENSITY VARIATIONS AND CROSS OVER POINTS BASED FEATURE EXTRACTION

Signature has its own advantage in person identification. The facts that people usually do not putting text in it; rather they draw a pattern as their signature. Even today, numbers of transactions are increasing related to banking and businesses are being identified via signatures. The main difficulty lies in the variations of the geometrical representation of the signature which is closely related to the identity of human beings. Hence, development methods for genuine signature verification must be needed. When bundles of documents, e.g. bank cheques, have to be verified in a limited time, the manual verification of account holders’ signatures is often tedious work. So there is a need of Automatic Signature Verification and Identification systems. For that different logic should be considered to process such signatures. The present paper is done in the field of offline signature identify by extracting some special domain features that make a signature difficult to forge. In this paper existing signature verification systems have been thoroughly studied and a model is designed to develop an offline signature idenfication system. Here off-line signature idenfication system that depends on high intensity variation based features as well as cross over points based features. Main aim is to take various feature points of a given signature and compares them with the test signatures feature points by choosing appropriate classifiers.

RAISING THE PERFORMANCE OF AUTOMATIC SIGNATURE VERIFICATION OVER THAT OBTAINABLE BY USING THE BEST FEATURE SET

For decades, published works on Automatic Signature Verification (ASV) that use threshold-based decision, depended on using one feature set for verification. Some researchers selected this feature set based on their experience, and others selected it using some feature selection algorithms that can select the best feature set (gives the highest performance). In practical systems, the signature data could be noisy, and recognition of the check writer in multi-signatory accounts is required. Due to the error caused by such requirements and data quality, improving the performance becomes a necessity. In this paper, a new technique for ASV decision making use of Multi-Sets of Features (MSF) is introduced. The new technique and its motivation are explained, and a precise evaluation of its efficiency is made. The experimental results have shown that the new technique gives important improvement in forgery detection and in the overall performance. This technique which was developed within an integrated plan of building a commercial offline ASV system to work in the actual USA banks environment was tested during the prototyping period with about 1000 signature samples, and has already been in use for years as a component of a cooperative decision making ASV system that tests over a million check signature every day without any false acceptance (False Acceptance = 0).

Antiforgery: a novel pseudo-outer product based fuzzy neural network driven signature verification system

A novel pseudo-outer product based fuzzy neural network (POPFNN-TVR) driven signature verification system called the antiforgery system is presented in this paper. As Plamondon and Lorette have stated that the design of a signature verification system generally requires the solution of five types of problems: data acquisition, preprocessing, feature extraction, comparison process, and performance evaluation. However, unlike most existing automatic signature verification systems which employ traditional techniques (i.e. image processing techniques) to solve these problems, the proposed system is constructed on the basis of a novel fuzzy neural network called the POPFNN-TVR. The characteristics of POPFNN-TVR, such as the learning ability, generalization ability, and high computational ability, make antiforgery particularly powerful when verifying skilled forgeries. To demonstrate the efficacy of POPFNN-TVR and its application in the antiforgery system, several types of experiments have been designed and implemented in this work. The experimental results and analysis are presented at the end of the paper for discussion.

Signature Verification: Increasing Performance by a Multi-Stage System

A serial three stage multi-expert system for facing the problem of signature verification is proposed. The whole decision process is organised into successive stages, each using a very reduced set of features for recognising forgeries and providing information about the reliability of the recognition process. The first expert, adopting only a single global feature, is devoted to the elimination of random and simple forgeries. The second stage receives only those signatures not classified as false by the first stage (i.e. those signatures really genuine or forgeries reproduced in a skilled way), and adopts a single specific feature suitable for isolating skilled forgeries. Both of these two stages employ suitable criteria for estimating the reliability of the performed classification, so that, in case of uncertainty, the signature is forwarded to a final stage which takes the final decision, taking into account the decisions of the previous stages together with the corresponding reliability estimations. The proposed multi-stage automatic signature verification system has been tested on a database of signatures produced by 49 different writers. The experimental analysis highlights the effectiveness of the approach: the proposed system employing only two features, used in distinct moments of the decision process, performs better than other systems, employing larger feature set (including the features used in the proposed system) and performing classification in a single stage.

AN EVALUATION OF PARALLEL STRATEGIES FOR FEATURE VECTOR CONSTRUCTION IN AUTOMATIC SIGNATURE VERIFICATION SYSTEMS

This paper describes possible strategies for the implementation of a feature selection algorithm particularly suited to the realisation of an efficient automatic handwritten signature verification system in which an active feature vector, optimised with respect to an individual signer, is constructed during an enrollment period. A range of configurations based on transputer arrays are considered and the possible implementational approaches evaluated. The paper demonstrates how the inherent parallelism which exists within a generic model for verification can be exploited to provide an optimised general-purpose framework for verification processing.

THE DESIGN OF AN ON-LINE SIGNATURE VERIFICATION SYSTEM: FROM THEORY TO PRACTICE

This paper reports on the major decisions that have to be made throughout the design phase of an automatic signature verification system. It shows how knowledge about signature control and generation, in terms of a realistic mathematical and psychophysical model, can be helpful in guiding and orienting design choices and related research activities. A system designed according to this methodology is described and the results of a robust forgery test are presented.

Automatic signature verification using a three-axis force-sensitive pen

A performance analysis of an automatic signature-verification system based upon the use of a three-axis force-sensitive pen to transduce the dynamics of the handwritten signature into electrical signals suitable for real-time computer processing is presented. False-rejection and imposter-acceptance (i.e. type I/type II error) curves are presented for a variety of operating conditions. For typical `real world' conditions, the equal-error rate (where type I error rate equals type II error rate) is about one percent. The performance analysis was based upon a data base of 5220 true signatures obtained from 58 subjects over a four- month period, and 648 attempted forgeries obtained from 12 forgers. The forgers were given copies of the true signers' signatures, told how the verification system operates and what it measures, allowed to watch video tapes with close-up views of the signatures to be forged as they were being written, and allowed to practice for a three-week period. Particular advantages of the signature-verification system reported here are high performance, low storage requirements (typically 200 to 300 bits per user), and low-cost implementation in a stand-alone microprocessor unit.