Signature Verification System Research Articles

Handwritten Signature Verification Using Wrist-Worn Devices

This paper suggests a novel verification system for handwritten signatures. The proposed system is based on capturing motion signals from the sensors of wrist-worn devices, such as smartwatches and fitness trackers, during the signing process, to train a machine learning classifier to determine whether a given signature is genuine or forged. Our system can be used to: (1) Verify signatures written on paper documents, such as checks, credit card receipts and vote by mail ballots. Unlike existing systems for signature verification, our system obtains a high degree of accuracy, without requiring an ad hoc digital signing device. (2) Authenticate a user of a secure system based on "who you are" traits. Unlike existing "motion-based" authentication methods that commonly rely on long-term user behavior, writing a signature is a relatively short-term process. In order to evaluate our system, we collected 1,980 genuine and forged signature recordings from 66 different subjects, captured using a smartwatch device. Applying our signature verification system on the collected dataset, we show that it significantly outperforms two other state-of-the-art systems, obtaining an EER of 2.36% and an AUC of 98.52%.

SM-DTW: Stability Modulated Dynamic Time Warping for signature verification

Building upon findings in computational model of handwriting learning and execution, we introduce the concept of stability to explain the difference between the actual movements performed during multiple execution of the subject’s signature, and conjecture that the most stable parts of the signature should play a paramount role in evaluating the similarity between a questioned signature and the reference ones during signature verification. We then introduce the Stability Modulated Dynamic Time Warping algorithm for incorporating the stability regions, i.e. the most similar parts between two signatures, into the distance measure between a pair of signatures computed by the Dynamic Time Warping for signature verification. Experiments were conducted on two datasets largely adopted for performance evaluation. Experimental results show that the proposed algorithm improves the performance of the baseline system and compares favourably with other top performing signature verification systems.

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.

Information Divergence-Based Matching Strategy for Online Signature Verification

The phenomenon of data clutter caused by intervariability (individual features) and intravariability (intrinsic noise of reference samples) is one of the most important reasons for performance degradation in online signature verification systems. To address this problem, we introduce information divergence in the field of signature verification to take full advantage of the information contained in all reference samples by shifting the distance measurement between the test and reference samples to a similarity measurement between two distributions (generated between reference samples as well as between the test sample and all reference samples) and simultaneously make full use of the spatial information of the reference samples. Based on that change, we propose a novel information divergence framework and provide some matcher instances that work within the proposed framework to effectively improve the performance of a signature verification system. Furthermore, to exploit the advantages of the new matching strategy, we propose a new dynamic time warping algorithm. In addition, we provide in-depth analysis of several distance normalizations and apply them to signature verification to reduce the adverse effect of “clutter” on signature data, which can effectively improve system performance. The experimental results on the MCYT-100 and SUSIG signature databases achieved equal error rates of 2.25% and 1.70% when 10 reference samples were used and 3.16% and 2.13% when 5 reference samples were used, respectively, illustrating the effectiveness of the proposed strategy in relation to other state-of-the-art strategies.

A framework for offline signature verification system: Best features selection approach

Abstract Biometric verification is a method of identifying the persons by their individualities or traits. Signature verification is the most generally used biometric to maintain human privacy. It is used in many areas as banking, access control, e-business etc. and equally important in financial transactions. Research has progressed greatly in the area of signature verification but still, it is hard to discriminate between genuine signatures and skilled forgeries. Based on the idea of best features selection, a novel technique is introduced in this article foran offline verification system. The presented system consists of four major steps: preprocessing, features extraction, features selection, and feature verification. Global features in the proposed work comprise of aspect ratio, the area of signature, pure width, pure height and normalized actual signature height. Local features consist of signature centroid, slope, angle, and distance. In features selection component, a genetic algorithm is utilized to find appropriate features set which are later on given to support vector machine for verification. For experimental analysis, the selected datasets are CEDAR, MCYT and GPDS synthetic. The performance of proposed algorithm is based on three accuracy measures as FAR, FRR and AER.

A real-world online signature verification system based on correlation algorithm

A real-world online signature verification system based on correlation algorithm

A real-world online signature verification system based on correlation algorithm

Handwriting signature is one of features of the biometric identification and authentication, which is used to verify or authenticate a 'signer' and the document signed. US law (the electronic signatures (E-Sign) in Global and National Commerce Act) and the United Nations Commission on International Trade Law Model Law on Electronic Commerce already grant electronic signatures legal validity equivalent to traditional hand-written counterparts. The intention of those laws is to cut costs while providing more stringent security, especially in the emerging e-commerce arena. In this paper, we (a) describe fundamentals and the current status of electronic signatures in detail. (b) Propose an online handwriting signature verification algorithm by using four online elements as the verification features (they are X axle, Y axle, pressure and velocity (time). Correlation algorithm is used to test and verify signatures. (c) Develop a real world application system with the online electronic signature.

Exploring Recurrent Neural Networks for On-Line Handwritten Signature Biometrics

Systems based on deep neural networks have made a breakthrough in many different pattern recognition tasks. However, the use of these systems with traditional architectures seems not to work properly when the amount of training data is scarce. This is the case of the on-line signature verification task. In this paper, we propose a novel writer-independent on-line signature verification systems based on Recurrent Neural Networks (RNNs) with a Siamese architecture whose goal is to learn a dissimilarity metric from the pairs of signatures. To the best of our knowledge, this is the first time these recurrent Siamese networks are applied to the field of on-line signature verification, which provides our main motivation. We propose both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) systems with a Siamese architecture. In addition, a bidirectional scheme (which is able to access both past and future context) is considered for both LSTM- and GRU-based systems. An exhaustive analysis of the system performance and also the time consumed during the training process for each recurrent Siamese network is carried out in order to compare the advantages and disadvantages for practical applications. For the experimental work, we use the BiosecurID database comprised of 400 users who contributed a total of 11,200 signatures in four separated acquisition sessions. Results achieved using our proposed recurrent Siamese networks have outperformed the state-of-the-art on-line signature verification systems using the same database.

Features selection for offline handwritten signature verification: state of the art

This research comes out with an in-depth review of widely used techniques to handwritten signature verification based, feature selection techniques. The focus of this research is to explore best features selection criteria for signature verification to avoid forgery. This paper further present pros and cons of local and global features selection techniques, reported in the state of art. Experiments are conducted on benchmark databases for signature verification systems (GPDS). Results are tested using two standard protocols; GPDS and the program for rate estimation and feature selection. The current precision of the signature verification techniques reported in state of art are compared on benchmark database and possible solutions are suggested to improve the accuracy. As the equal error rate is an important factor for evaluating the signature verification's accuracy, the results show that the feature selection methods have successfully contributed toward efficient signature verification.

A combine approach of preprocessing in integrated signature verification (ISV)

For last few decades, signature verification is an important area of research. Recently, integrated signature verification (ISV) comes in a play, in which dynamic and static both signatures verified for the forgery [2]. In integrated signature verification system, initial start with a data acquisition stage which can be done from both handwritten and with the use of stylus. Then next step is pre-processing of the signature to make the image noise free and easy to extract. Third and most important step that is the feature extraction. In this step we find that images have different types of features such as local, global, geometrical, and statistical and projection. Last and Final step, which is a crucial step on which the whole system depends that is the verification, where the forgery factor has been found in terms of FAR, FRR, EAR to calculate the performance of the system. Many techniques and filters have been already used to remove the noise in the signature verification system. We proposed a system on integrated pre-processing of the signature. While scanning the signature, some noise is added, which gives the blur image for feature extraction. To improve the system performance and fine feature extraction, we develop a system for integrated pre-processing. In addition, current methods used for features extraction and approaches used for verification in signature systems are also presented. In conclusion, we suggest some encouraging ideas to be incorporated in the future.

Finger or Stylus: Their Impact on the Performance of On-line Signature Verification Systems

Abstract The widespread use of smartphones and the ability of these devices to digitize signatures have made it possible to sign electronic documents in this way. In this paper we compared two on-line signature databases in terms of verification performance: the MCYT containing signatures drawn by stylus pen, and MOBISIG containing finger drawn signatures. Performance evaluations were performed using both local and global systems. In the case of global systems, we evaluated the performance of a novel information theory features set. Little improvement was achieved by this feature set. There were large differences between the two databases in terms of performance. Finger drawn signatures collected by mobile device were proved inferior to signatures collected by digitizing tablet and its stylus.

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.

A novel database for automatic processing of Persian handwritten bank checks

This paper introduces a database of Persian handwritten bank checks. The database includes legal amounts, courtesy amounts, dates, receiver names, signatures and account numbers. In addition to checks, the database also comprises handwritten forms with words used in legal amounts, digits employed in the courtesy amounts and signatures of contributors. The database can be employed for evaluation of segmentation and recognition of different fields in checks as well as for verification of signatures. Data is collected and organized in two series. The first series, comprising 500 hand filled Persian checks and 500 forms contributed by 500 different individuals, supports evaluation of segmentation and recognition tasks. The second series contributed by 100 writers supports evaluation of signature verification systems and comprises 200 genuine checks, 100 forms and 200 forged checks. To provide an insight to other researchers, experiments have been carried out on recognition of dates and courtesy amounts with recognition rates of 76% and 73%, respectively. Likewise, experiments on signature verification with skilled forgeries realized an average error rate of 12.25%. The database, named Persian Handwriting Bank Checks (PHBC) Database, is freely available to the research community upon request to the authors. It is expected that the developed database will not only allow researchers working on automatic processing of Persian checks to evaluate their proposed techniques but will also serve to meaningfully compare different recognition and verification techniques.

Handwritten signature verification using the quad-tree histogram of templates and a Support Vector-based artificial immune classification

This work proposes a novel system for off-line handwritten signature verification. A new descriptor founded on a quad-tree structure of the Histogram Of Templates (HOT) is introduced. For the verification step, we propose a robust implementation of the Artificial Immune Recognition System (AIRS). This classifier is inspired from the natural immune system, which generates antibodies to protect the human body against antigens. The AIRS training develops new memory cells that are subsequently used to recognize data through a k Nearest Neighbor (kNN) classification. Presently, to get a robust verification, the kNN classification is substituted by a Support Vector (SV) decision, yielding the AIRSV classifier. Experiments are performed on three datasets, namely, MCYT-75, GPDS-300 and GPDS-4000. AIRSV performance is assessed comparatively to both conventional AIRS as well as SVM. Obtained results demonstrated that AIRSV is more effective than classical AIRS. Moreover, the proposed signature verification system gives similar and sometimes better performance than SVM as well as the state-of-the-art methods.

Fuzzy Logic Based Adaptive Resonance Theory-1 Approach for Offline Signature Verification

Abstract This paper presents the use fuzzy logic with adaptive resonance theory-1 in signature verification. Fuzzy model is capable of stable learning of recognition categories in response to arbitrary sequences of binary input pattern. The work was carried out on two famous available signature corpuses i.e. MCYT (Online Spanish signatures database) and GPDS (Grupo de Procesado Digital de la se?al). Local binary patterns (LBP) and Gray Level Co-occurrence Matrices (GLCM) features were calculated for robust offline signature verification system. Training and verification was done using fuzzy adaptive resonance theory-1(FART-1). The system is trained and verified for different datasets to increase the accuracy of the classifier. The results thus obtained are robust than other existing techniques. The FAR and FRR for the system is 0.74% and 0.83% respectively.

A Clustering-based Offline Signature Verification System for Managing Lecture Attendance

A Clustering-based Offline Signature Verification System for Managing Lecture Attendance

Benchmarking desktop and mobile handwriting across COTS devices: The e-BioSign biometric database.

This paper describes the design, acquisition process and baseline evaluation of the new e-BioSign database, which includes dynamic signature and handwriting information. Data is acquired from 5 different COTS devices: three Wacom devices (STU-500, STU-530 and DTU-1031) specifically designed to capture dynamic signatures and handwriting, and two general purpose tablets (Samsung Galaxy Note 10.1 and Samsung ATIV 7). For the two Samsung tablets, data is collected using both pen stylus and also the finger in order to study the performance of signature verification in a mobile scenario. Data was collected in two sessions for 65 subjects, and includes dynamic information of the signature, the full name and alpha numeric sequences. Skilled forgeries were also performed for signatures and full names. We also report a benchmark evaluation based on e-BioSign for person verification under three different real scenarios: 1) intra-device, 2) inter-device, and 3) mixed writing-tool. We have experimented the proposed benchmark using the main existing approaches for signature verification: feature- and time functions-based. As a result, new insights into the problem of signature biometrics in sensor-interoperable scenarios have been obtained, namely: the importance of specific methods for dealing with device interoperability, and the necessity of a deeper analysis on signatures acquired using the finger as the writing tool. This e-BioSign public database allows the research community to: 1) further analyse and develop signature verification systems in realistic scenarios, and 2) investigate towards a better understanding of the nature of the human handwriting when captured using electronic COTS devices in realistic conditions.

Signature Verification System

Signature Verification System

Signature Verification Based on the Kinematic Theory of Rapid Human Movements

When using tablet computers, smartphones, or digital pens, human users perform movements with a stylus or their fingers that can be analyzed by the kinematic theory of rapid human movements. In this paper, we present a user-centered system for signature verification that performs such a kinematic analysis to verify the identity of the user. It is one of the first systems that is based on a direct comparison of the elementary neuromuscular strokes which are detected in the handwriting. Taking into account the number of strokes, their similarity, and their timing, the string edit distance is employed to derive a dissimilarity measure for signature verification. On several benchmark datasets, we demonstrate that this neuromuscular analysis is complementary to a well-established verification using dynamic time warping. By combining both approaches, our verifier is able to outperform current state-of-the-art results in on-line signature verification.