Online Signature Verification System Research Articles

Kruskal Wallis and mRMR Feature Selection based Online Signature Verification System using Multiple SVM and KNN

Signature verification is a very important research area. Signature has been widely accepted as a person authentication method for centuries. It is mostly used in financial transactions, document authentication and agreements. It is more susceptible to being forged than any other biometrics. Online signature verification is used in real-time applications like e-commerce, online resource access, online financial transactions, physical access into a restricted area and many more. In order to achieve high efficiency in online signature verification systems, feature extraction and feature selection play a significant role. A suitable signature verification system is needed to prevent forgery and accept the genuine signer. We have extracted 30 global features from all 40 signers for verification. Here, k fold cross-validation technique is used to enhance the model's performance on unseen data. User-specific feature selection and ranking are done using Kruskal Wallis and Minimum Redundancy Maximum Relevance (mRMR) algorithm to hunt which performs better in our case. Kruskal-Wallis method tests if two or more classes have an equal median and gives the value of P based on which discriminative features are selected, whereas the mRMR algorithm ranks the whole feature set according to its importance. It evaluates the relevance of a feature and penalizes redundancy. Finally, multiple SVM and KNN classifiers are trained and tested with various selected features using Kruskal Wallis and mRMR to determine which combination performs best for the online signature verification system. Our model is trained, validated and tested on the SVC 2004 Task 1 database, which consists of skill forgery signatures. Here, one to one verification is done using each user's genuine and skill forgery signatures, which is the hardest to detect. Best average testing accuracy achieved in our case is 90.25% using Weighted KNN and Kruskal Wallis selected 15 features.

Acoustic Sensing Based on Online Handwritten Signature Verification

Handwritten signatures are widely used for identity authorization. However, verifying handwritten signatures is cumbersome in practice due to the dependency on extra drawing tools such as a digitizer, and because the false acceptance of a forged signature can cause damage to property. Therefore, exploring a way to balance the security and user experiment of handwritten signatures is critical. In this paper, we propose a handheld signature verification scheme called SilentSign, which leverages acoustic sensors (i.e., microphone and speaker) in mobile devices. Compared to the previous online signature verification system, it provides handy and safe paper-based signature verification services. The prime notion is to utilize the acoustic signals that are bounced back via a pen tip to depict a user's signing pattern. We designed the signal modulation stratagem carefully to guarantee high performance, developed a distance measurement algorithm based on phase shift, and trained a verification model. In comparison with the traditional signature verification scheme, SilentSign allows users to sign more conveniently as well as invisibly. To evaluate SilentSign in various settings, we conducted comprehensive experiments with 35 participants. Our results reveal that SilentSign can attain 98.2% AUC and 1.25% EER. We note that a shorter conference version of this paper was presented in Percom (2019). Our initial conference paper did not finish the complete experiment. This manuscript has been revised and provided additional experiments to the conference proceedings; for example, by including System Robustness, Computational Overhead, etc.

SVC-onGoing: Signature verification competition

This article presents SVC-onGoing11https://competitions.codalab.org/competitions/27295., an on-going competition for on-line signature verification where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases, such as DeepSignDB22https://github.com/BiDAlab/DeepSignDB. and SVC2021_EvalDB33https://github.com/BiDAlab/SVC2021_EvalDB., and standard experimental protocols. SVC-onGoing is based on the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021), which has been extended to allow participants anytime. The goal of SVC-onGoing is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC-onGoing prove the high potential of deep learning methods in comparison with traditional methods. In particular, the best signature verification system has obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). Future studies in the field should be oriented to improve the performance of signature verification systems on the challenging mobile scenarios of SVC-onGoing in which several mobile devices and the finger are used during the signature acquisition.

Online Signature Verification Using Locally Weighted Dynamic Time Warping via Multiple Fusion Strategies

Online signature verification is the process of using a dynamic signature verification system to confirm the writer’s identity. It can be used as a security system to confirm entrance applications and password substitutes, as well as a forensic tool to assist an expert’s investigation. This study proposes a novel online signature verification system based on a single-template strategy to improve performance in real-world scenarios. It employs discriminative mean signature template sets as well as fusion strategies of multiple local weighting and warping schemes, for dynamic time warping (DTW). The first step is to generate a set of user-specific mean signature templates for each feature using a recent time-series averaging method, namely, Euclidean barycenter-based DTW barycenter averaging. Then, using multiple and direct matching points between the mean signature templates and references for dependent and independent DTW, we obtain a local weighting estimate considering local stability sequences. Furthermore, we develop fusion strategies for calculating locally weighted DTW sets and concatenating them as a feature vector for each warping, followed by the construction of a support vector machine (SVM) classifier. Finally, in the verification phase, we use the single-template technique to compute a discriminative fused score using SVMs between the mean signature template sets and a query sample. The effectiveness of the proposed method is demonstrated by extensive experimental results obtained using three public online signature datasets: SVC2004 Task1/Task2 and MCYT-100.

Online Signature Verification Systems on a Low-Cost FPGA

This paper describes three different approaches for the implementation of an online signature verification system on a low-cost FPGA. The system is based on an algorithm, which operates on real numbers using the double-precision floating-point IEEE 754 format. The double-precision computations are replaced by simpler formats, without affecting the biometrics performance, in order to permit efficient implementations on low-cost FPGA families. The first approach is an embedded system based on MicroBlaze, a 32-bit soft-core microprocessor designed for Xilinx FPGAs, which can be configured by including a single-precision floating-point unit (FPU). The second implementation attaches a hardware accelerator to the embedded system to reduce the execution time on floating-point vectors. The last approach is a custom computing system, which is built from a large set of arithmetic circuits that replace the floating-point data with a more efficient representation based on fixed-point format. The latter system provides a very high runtime acceleration factor at the expense of using a large number of FPGA resources, a complex development cycle and no flexibility since it cannot be adapted to other biometric algorithms. By contrast, the first system provides just the opposite features, while the second approach is a mixed solution between both of them. The experimental results show that both the hardware accelerator and the custom computing system reduce the execution time by a factor ×7.6 and ×201 but increase the logic FPGA resources by a factor ×2.3 and ×5.2, respectively, in comparison with the MicroBlaze embedded system.

Signature barcodes for online verification

As a sub-branch of behavioral biometrics, online signature verification systems deal with unique signing characteristics, which could be better differentiated by extraction of habitual singing styles instead of geometric features in case of perfect forgery. Even if the signatures are geometrically identical, speed and frequency components of the signing process might significantly vary. Therefore, a novel framework is introduced as a new signature verification protocol for touchscreen devices using barcodes containing the dominant frequency component of the speed signals. A special interface is designed as signature tracker to extract the displacement data sampled from the signing process. The speed signals are interpolated from the displacement data and the frequency components of the signals are computed by scalograms analysis governed by continuous wavelet transformations (CWT). The signature barcodes are generated as 4-scale scalograms and classified by support vector machines (SVM). Among several compatible wavelets, Gaussian derivative wavelet is selected for generating scalograms and the results of the process are calculated as 2.25% FAR, 2.75% FRR and 2.81%EER for our dataset. The framework is also tested with SVC2004 data that we achieved 0% FAR, 9.33% FRR and 8%EER, also with SUSIG-Visual, SUSIG-Blind, MOBISIG databases and we reached between 1.22%-3.62% average EERs, which are competitive among the relevant results. Given the promising outcomes, the signature barcoding is very reliable method which could be executed by a simple touchscreen interface collecting the barcodes for storing and benchmarking when needed.

Smartphone-based Handwritten Signature Verification using Acoustic Signals

Handwritten signature verification techniques, which can facilitate user authentication and enable secure information exchange, are still important in property safety. However, on-line automatic handwritten signature verification usually requires dynamic handwritten patterns captured by a special device, such as a sensor-instrumented pen, a tablet or a smartwatch on the dominant hand. This paper presents SonarSign, an on-line handwritten signature verification system based on inaudible acoustic signals. The key insight is to use acoustic signals to capture the dynamic handwritten signature patterns for verification. Particularly, SonarSign exploits the built-in speakers and microphones of smartphones to transmit a specially designed training sequence and record the corresponding echo for channel impulse response (CIR) estimation, respectively. Based on the sensitivity of CIR to the tiny surrounding environment changes including handwritten signature actions, SonarSign designs an attentional multi-modal Siamese network for end-to-end signatures verification. First, multi-modal CIR streams are fused to extract representative signature pattern features from spatio-temporal dimensions. Then an attentional Siamese network is elaborated to verify whether the given two signatures are from the same signatory. Extensive experiments in real-world scenarios show that SonarSign can achieve accurate and robust signatures verification with an AUC (Area Under ROC (Receiver Operating Characteristic) Curve) of 98.02% and an EER (Equal Error Rate) of 5.79% for unseen users.

Online signature verification using signature down-sampling and signer-dependent sampling frequency

AbstractOnline signature verification considers signatures as time sequences of different measurements of the signing instrument. These signals are captured on digital devices and therefore consist of a discrete number of samples. To enrich or simplify this information, several verifiers employ resampling and interpolation as a preprocessing step to improve their results; however, their design decisions may be difficult to generalize. This study investigates the direct effect of the sampling rate of the input signals on the accuracy of online signature verification systems without using interpolation techniques and proposes a novel online signature verification system based on a signer-dependent sampling frequency. Twenty verifier configurations were created for five different public signature databases and a variety of popular preprocessing approaches and evaluated for 20–40 different sampling rates. Our results show that there is an optimal range for the sampling frequency and the number of sample points that minimizes the error rate of a verifier. A sampling frequency range of 15–50 Hz and a signature point count of 60–240 provided the best accuracies in our experiments. As expected, lower ranges showed inaccurate results; interestingly, however, higher frequencies often decreased the verification accuracy. The results show that one can achieve better or at least the same verification accuracies faster by down-sampling the online signatures before further processing. The proposed system achieved competitive results to state-of-the-art systems for different databases by using the optimal sampling frequency. We also studied the effect of choosing individual sampling frequencies for each signer and proposed a signature verification system based on signer-dependent sampling frequency. The proposed system was tested using 500 different verification methods and improved the accuracy in 92% of the test cases compared to the usage of the original frequency.

A new wrapper feature selection method for language-invariant offline signature verification

Among various biometric systems, an offline signature verification system has been widely used in all fields such as in banks, educational institutes, legal procedures and, criminal investigation where authentication and verification are utmost required. Despite the popularity of the online signature verification system, its offline counterpart still has great importance in developing countries, especially in rural areas, where easy availability of smart devices along with fast internet connection is not available. In this work, we have developed a language invariant offline signature verification model which is almost equally applicable for both writer dependent and writer independent scenarios. At first, an offline signature is collected as an image, following which a corresponding signal is generated using singular value decomposition. Then four different kinds of features namely, statistical, shape-based, similarity-based, and frequency-based are extracted from the transformed signal of the signature image. Next, to reduce the feature dimension, we have designed a novel wrapper feature selection method based on Red Deer Algorithm, a recently proposed meta-heuristic method, to keep only the relevant features to be used during signature authentication and verification process. Finally, a confidence score from the Naïve Bayes classifier has been used to perform the authentication and verification process. Our model has been evaluated on CEDAR (English), UTSig (Persian), Sigcomp 2011 Dutch, Sigcomp 2011 Chinese, and SigWIcomp 2015 Bengali signature datasets. Obtained results confirm that the proposed model can outperform many of its predecessors.

A two-tier ensemble approach for writer dependent online signature verification

Biometric verification systems are used to recognize people based on their uniqueness or characteristics. Signature is considered as one of the most commonly used biometric that individualizes a human being. It is generally used to keep individual’s privacy in many places such as banking sectors, academic institutes, office premises and trading. But increase of criminal attempts in falsifying an individual’s signature, known as signature forgeries, motivates the researchers to develop computerized systems that can verify the genuineness of a questioned signature. Though many researches have been performed till date, but the issue of identifying skilled forgeries still remains a major concern for the researchers. To this end, in this work, we have designed a two-tier ensemble based writer dependent and language- invariant online signature verification system. In doing so, we have first extracted three different categories of features from each input signature: physical, frequency based and statistical, and then designed a feature-classifier based ensemble (i.e., Ensemble#1) using seven different classifiers. The predictions obtained from the seven classifiers are combined using normalised distribution summation strategy. Decisions obtained from Ensemble#1 are then fed to Ensemble#2, where a majority voting based approach is followed, to identify the input signature as genuine or forged. Our system is evaluated on two standard datasets: SVC 2004 (Task-II) and MCYT-100 in a writer dependent way. The equal error rate (ERR) and accuracy on SVC 2004 dataset are 2.20 and 98.43% respectively, and on MCYT-100 dataset these are 2.84 and 97.87% respectively. The GAR@0.01FAR value obtained for the SVC-2004 dataset is 94.50% while it is 92.90% for MCYT-100 dataset. We have also compared our results with some state-of-the-art methods, and it has been found that our method performs better than most of these methods. The code of this work is available at: https://github.com/prat1999/Online_Signature_Verification .

OSVFuseNet: Online Signature Verification by feature fusion and depth-wise separable convolution based deep learning

Online Signature Verification (OSV) techniques have been deployed in production systems for decades, yet training the model for efficient classification of the test signature from fewer training signature samples is still an open challenge. The advancements in Convolutional Neural Networks (CNNs) enormously boosted the effectiveness of OSV systems. However, learning subtle and discriminating representations from few training samples to classify the genuineness of test signature has not been explored fully. In this paper, a Convolution Autoencoder (CAE) is used to obtain high-level feature representations from the input signature and these high level features are fused with handcrafted features to constitute a hybrid feature set. The hybrid set of features is presented as an input to an Online Signature Verification framework made up of Depth-wise Separable Convolutional Neural Network (DWSCNN). DWSCNN effectively learn deep feature representations with fewer training samples and parameters than traditional CNNs resulting in a light weight OSV framework. Thorough experimental analysis on three benchmark datasets MCYT-100 (DB1), SVC-2004-Task2 and SUSIG-Visual corpus confirm that the proposed hybrid fusion of feature set and DWSCNN based OSV framework achieve higher classification accuracies and outperforms many contemporary and state-of-the art OSV models.

Signature Verification Using Critical Segments for Securing Mobile Transactions

The explosive usage of mobile devices enables conducting electronic transactions involving direct signature on such devices. Thus, user signature verification becomes critical to ensure the success deployment of online transactions such as approving legal documents and authenticating financial transactions. Existing approaches mainly focus on user verification targeting the unlocking of mobile devices or performing continuous verification based on a user's behavioral traits. Few studies provide efficient real-time user signature verification. In this work, we propose a critical segment based online signature verification system to secure mobile transactions on multi-touch mobile devices. Our system identifies and exploits the segments which remain invariant within a user's signature to capture the intrinsic signing behavior embedded in each user's signature. Our system extracts useful features from a user's signature that describe both the geometric layout of the signature as well as behavioral and physiological characteristics in the user's signing process. Given the input signatures for user enrollment, our system further designs a quality score to identify the problematic signature sets to achieve robust user signature profile construction. Moreover, we develop the signature normalization and interpolation methods to achieve robust signature verification in the presence of signature geometric distortions caused by different writing sizes, orientations and locations on touch screens. Our experimental evaluation of 25 subjects over six months time period shows that our system is highly accurate in provide signature verification and robust to signature forging attacks.

Online signature verification using single-template matching with time-series averaging and gradient boosting

In keeping with recent developments in artificial intelligence in the era of big data, there is a demand for online signature verification systems that operate at high speeds, provide a high level of security, and allow high tolerances while achieving sufficient performance. In response to these needs, the present study proposes a novel, single-template strategy using a mean template set and weighted multiple dynamic time warping (DTW) distances for a function-based approach to online signature verification. Specifically, to obtain an effective mean template for each feature while reflecting intra-user variability between all the reference samples, we adopt a novel time-series averaging method based on Euclidean barycenter-based DTW barycenter averaging. Then, by using the mean template set, we calculate multiple DTW distances from multivariate time series based on dependent and independent warping. Finally, to boost the discriminative power, we apply a weighting scheme using a gradient boosting model to efficiently combine the multiple DTW distances. Experimental results using the common SVC2004 Task1/Task2 and MCYT-100 signature datasets confirm that the proposed method is effective for online signature verification.

Development of a System with Online Signature Biometric for Access Control Application

Online signature verification system is a technique based on behavioural biometrics which is gaining popularity for its widespread acceptability due to ease of use and forge proof features. Online (dynamic) signatures are captured from pressure sensitive tablets, signature pads, tablet PCs for further processing. This paper depicts the working mechanism of such a system capable of verifying the true signers as well as detecting the forgery attempt. After the signature data collection, the signatures were preprocessed for future use. A set of total 58 different features (local, global and normalized) were extracted from the online signature data. The system presents a binary classification technique since the test signature is categorized into one of the two classes (genuine or false). Template matching based Dynamic Time Warping (DTW) classifier is used for training and testing purposes with an average recognition rate of 90% for true signers. A separate version of the system was also developed which blocks random and skilled forgeries with a high level of accuracy though the TN rate is higher than the system described here.

ASSV

As one kind of biological characteristics of people, handwritten signature has been widely used in the banking industry, government and education. Verifying handwritten signatures manually causes too much human cost, and its high probability of errors can threaten the property safety and even society stability. Therefore, the need for an automatic verification system is emphasized. This paper proposes a device-free on-line handwritten signature verification system ASSV, providing paper-based handwritten signature verification service. As far as we know, ASSV is the first system which uses the changes of acoustic signals to realize signature verification. ASSV differs from previous on-line signature verification work in two aspects: 1. It requires neither a special sensor-instrumented pen nor a tablet; 2. People do not need to wear a device such as a smartwatch on the dominant hand for hand tracking. Differing from previous acoustic-based sensing systems, ASSV uses a novel chord-based method to estimate phase-related changes caused by tiny actions. Then based on the estimation, frequency-domain features are extracted by a discrete cosine transform (DCT). Moreover, a deep convolutional neural network (CNN) model fed with distance matrices is designed to verify signatures. Extensive experiments show that ASSV is a robust, efficient and secure system achieving an AUC of 98.7% and an EER of 5.5% with a low latency.

Biometric signature authentication using machine learning techniques: Current trends, challenges and opportunities

Biometric systems are playing a key role in the multitude of applications and placed at the center of debate in the scientific research community. Among the numerous biometric systems, handwritten signature verification has got keen interest over the last three decades. Handwritten signature verification is the behavioral bio-metric system that discriminates the genuine signature from the pre-stored known signatures. It has been researched in the number of application areas like banking, financial and business transactions, cheque processing, access control and e-business etc. In this article, we surveyed the techniques of offline and online signature verification systems according to the taxonomy of classification model. A detailed background of signature verification system along with the available datasets are presented comprehensively. At the end, we presented the most notable challenges that guide the readers towards the current trends and future directions of the domain.

A Dempster–Shafer theory based classifier combination for online Signature recognition and verification systems

With the advancement in technology, the society demands a robust method for person authentication. Traditional authentication methods are based on the person’s knowledge such as PIN, passwords, and tokens etc. However, such methods are prone to steal and forgotten risks. Therefore, an efficient method for person identification and verification is required. In this paper, we present a novel biometric approach for online handwritten signature recognition and verification using Dempster–Shafer theory (DST). DST has been used effectively for combination of different information sources which provide incomplete, and complementary knowledge. Initially, signature identification and verification processes have been carried out using two different classifiers, namely, Hidden Markov Model (HMM) and Support Vector Machine (SVM). Next, the performance in terms of accuracy and the reliability of the system has been increased using DST by combining the probabilistic outputs of SVM and HMM classifiers. The feasibility of the approach has been tested on MCYT DB1 and SVC2004 biometric public databases for Latin script and a new online signature dataset for Devanagari script. To our knowledge there exist no dataset on online signature available in Devanagari script. Experimental results shows that the present approach is efficient in recognition and verification of signatures and outstrips existing work in this regard till date.

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.

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.

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

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