Online Signature Verification Research Articles

Feature Ranking Using Novel Consistency Measure by Normalized Standard Deviation and Proposal of Three Novel Global Features for Online Signature Verification

Signature is a behavioral biometric that evolves throughout a person’s life. Feature extraction and ranking are very important steps towards online signature verification in order to achieve high efficiency. In our case, we have extracted 48 global features. A novel feature ranking technique based on consistency measure using normalized standard deviation is proposed here and is compared with well-established mRMR based ranking. The use of normalized standard deviation formula is the novel approach for feature ranking. Moreover, we have proposed three novel global features for consistency measure and mRMR based ranking. These three features has shown its importance in consistency based ranking as well as in mRMR based ranking. Consistency estimation of global features is important in one-class classification framework, where only the genuine signatures are available. All the features that are ranked by consistency measures and its weighting factors of feature vector are computed for every signer. More consistent features has given more weight for verification. Feature ranking shows the importance of each features for a particular system. Also it helps in feature selection to select the more discriminating features for a particular system and removing all irrelevant features. It saves the computational time and size of the model. Different global features has different scaling factor. Generally, normalization is done before ranking but in our technique we have ranked our features without normalization. Because normalization disrupt the statistical consistency. Therefore, we have used min-max normalization where all the features are converted to (0-1) range after ranking. In our proposed system, the proposed phase related global features shows more consistency in our ranking process. Similarly well-established mRMR feature ranking has also ranked our proposed novel features number 48 (Standard deviation of the phase), 43 (Entropy of shape signature function) and 47 (Mean of phase) within top 13 features. It is seen that both our proposed novel feature ranking technique based on consistency measure and well-established mRMR based ranking has shown almost similar performance. The proposed algorithms are verified with SVC 2004 database.

Relative position matrix and multi-scale feature fusion for writer-independent online signature verification

Online signature verification (OSV) is widely used in finance, law and other fields, and is one of the important research projects on biological characteristics. However, its data set has a small scale and has high requirements for generalization of certification models. Therefore, how to overcome these problems is of great value to improve the practicality and security of online handwriting signature technology. We propose a writer-independent online handwritten signature verification method, which adopts the relative position matrix method to convert the traditional temporal features into images for processing. This method enriched the features of the signatures, serving the purpose of data augmentation. Then two-dimensional multi-scale feature fusion based Siamese neural network (2D-MFFnet) is built for representing and learning the importance of each channel adaptively combined with the attention mechanism. Finally, a temporal convolutional network is designed to construct the classifier. The results illustrate that compared with traditional time series models, the algorithm has reduced the equal error rate by at least 2.52 % on the open datasets MCYT-100 and SVC2004 task2.

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.

Inf-Att-OSVNet: information theory based feature selection and deep attention networks for online signature verification

Inf-Att-OSVNet: information theory based feature selection and deep attention networks for online signature verification

Online Signature Biometrics for Mobile Devices.

This paper addresses issues concerning biometric authentication based on handwritten signatures. Our research aimed to check whether a handwritten signature acquired with a mobile device can effectively verify a user's identity. We present a novel online signature verification method using coordinates of points and pressure values at each point collected with a mobile device. Convolutional neural networks are used for signature verification. In this paper, three neural network models are investigated, i.e., two self-made light SigNet and SigNetExt models and the VGG-16 model commonly used in image processing. The convolutional neural networks aim to determine whether the acquired signature sample matches the class declared by the signer. Thus, the scenario of closed set verification is performed. The effectiveness of our method was tested on signatures acquired with mobile phones. We used the subset of the multimodal database, MobiBits, that was captured using a custom-made application and consists of samples acquired from 53 people of diverse ages. The experimental results on accurate data demonstrate that developed architectures of deep neural networks can be successfully used for online handwritten signature verification. We achieved an equal error rate (EER) of 0.63% for random forgeries and 6.66% for skilled forgeries.

Compactnet: a lightweight convolutional neural network for one-shot online signature verification

Compactnet: a lightweight convolutional neural network for one-shot online signature verification

An Efficient, Fast and Accurate Online Signature Verification Using Blended Feature Vector and Deep Learning

Online signature verification is one of the biometric authentication techniques. Online signature verification reduces the human error that may occur due to physical signature verification. Many researchers have provided a method of online signature verification still there is a scope for improvement. In this paper, a novel blended feature vector (BFV) is formed by combining two feature vectors. The first of these feature vectors is formed using a raw online signature database and the other feature vector is formed of the features extracted from the grayscale images obtained from the signature database. The algorithm makes use of the probability distribution of the features to provide a feature vector that results in an enhanced verification system. Two online signature databases, namely SVC2004 and ATVS-SSig are used. BFV is used to train the proposed mixed sequence deep neural network (MS-DNN). The use of a blended feature vector in the training of MS-DNN, instead of a whole online signature database, improves the training time of deep neural network to a great extent. This reduction in training time might be useful for new user registration. The performance parameters considered are validation efficiency and equal error rate. Results are compared with the existing state of technology and the comparison shows that the use of a blended feature vector as a classification vector improves the validation efficiency (99.5%) and also there is an improvement in the verification success rate in terms of equal error rate (EER 1.5%) as compared to the existing research.

Spatio-temporal trajectory data modeling for fishing gear classification

International Organizations urge the protection of our oceans and their ecosystems due to their immeasurable importance to humankind. Since illegal fishing activities, commonly known as IUU fishing, cause irreparable damage to these ecosystems, concerned organisms are pushing to detect and combat IUU fishing practices. The automatic identification system allows to locate the position and trajectory of fishing vessels. In this study we address the task of detecting vessels’ fishing gears based on the trajectory behavior defined by GPS position data, a useful task to prevent the proliferation of IUU fishing practices. We present a new database including trajectories that span 7 different fishing gears and analyze these as in a time sequence analysis problem. We leverage from feature extraction techniques from the online signature verification domain to model vessel trajectories, and extract relevant information in the form of both local and global feature sets. We show how, based on these sets of features, the kinematics of vessels according to different fishing gears can be effectively classified using common supervised learning algorithms with accuracies up to 90%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$90\\%$$\\end{document}. Furthermore, motivated by the concerns raised by several organizations on the adverse impact of bottom trawling on marine biodiversity, we present a binary classification experiment in which we were able to distinguish this kind of fishing gear with an accuracy of 99%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$99\\%$$\\end{document}. We also illustrate in an ablation study the relevance of factors such as data availability and the sampling period to perform fishing gear classification. Compared to existing works, we highlight these factors, especially the importance of using sampling periods in the order of minutes instead of hours.

Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-2 with HMM Classifier

Online signature verification stands out as a distinctive biometric feature, offering both static and dynamic attributes within 2D signature images. A Hybrid Wavelet Transform-2 (HWT-2) with a size of 256 is constructed by employing the Kronecker product of two orthogonal transforms: DCT, DHT, Haar, Hadamard, and Kekre, each with sizes of 4 and 64. The HWT enables the analysis of signals at both global and local levels, akin to wavelet transforms. HWT-2 is applied to 256 samples of online handwritten signatures, and the first 128 samples of the output are utilized as feature vectors for the verification and forgery detection of online handwritten signatures. These feature vectors are inputted into Left-Right and Ergodic Hidden Markov Model (HMM) classifiers for analysis. The HMMs are trained using 10 randomly selected genuine signature samples, and subsequently tested on the remaining 10 genuine signatures and 20 forged signatures from 40 users of the SVC 2004 signature database. This process is repeated 20 times, and the average values are computed. Among all possible combinations of HWT-2 using DCT, DHT, Haar, Hadamard, and Kekre transforms for the Left-Right HMM model, the combination of DCT 4 and DHT 64 demonstrates the best performance, with False Rejection Rate (FRR) and False Acceptance Rate (FAR) values of 3.96% and 1.48%, respectively, for state 5. Similarly, for the Ergodic HMM model, the combination of DCT 4 and DHT 64 exhibits the best performance, with FRR and FAR values of 1.10% and 2.88%, respectively, for state 5. These results indicate that combinations of HWT-2 outperform individual orthogonal transforms, and further, that HWT-2 combinations within the Ergodic HMM model offer superior performance compared to the Left-Right HMM model.

Comparative Analysis of Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-1 and 2 with HMM Classifier

Online signature verification is a unique biometric feature. Provides static and dynamic features for 2D signature images. Hybrid wavelet transform -1 and 2 (HWT-1 and HWT-2) of size 256 is created using the Kronecker product of two orthogonal transforms such as DCT, DHT, Haar, Hadamard and Kekre with size 4 and 64. HWT has the ability to analyze signals such as wavelet transform at global and local levels. HWT-1 and HWT-2 are used for the 256 samples of the online Handwritten signature and the first 128 samples of the output are used as feature vectors for handwritten online signature verification and forgery detection. This feature vector is given to the Left-Right and Ergodic modelled HMM classifier for analysis. HMM is trained using 10 randomly chosen genuine signature samples and is used to test remaining 10 genuine signatures and 20 forged signatures of 40 users of SVC 2004 signature database. This process is iterated 20 times and then average values are calculated. Considering all the possible combination of HWT-1 and HWT-2 for DCT, DHT, Haar, Hadamard and Kekre transform for Left Right HMM model, DCT 4 Haar 64 HWT-1 offers best performance of FRR, FAR of 1.05%, 0.99% respectively for state 3. Considering all the possible combination of HWT-1 and HWT-2 for DCT, DHT, Haar, Hadamard and Kekre transform for Ergodic HMM model, DCT 4 DHT 64 offers best performance of FRR, FAR of 1.10%, 2.88% respectively for state 5. We conclude that comparing HWT-1 and HWT-2 combinations for Left Right and Ergodic HMM model, HWT-1 for Left Right model offer better performance.

Handwritten online signature verification and Forgery Detection for Online Handwritten Signature using Hybrid Wavelet Transform-1 with HMM classifier

Online signature verification employs a distinctive biometric trait by utilizing both static and dynamic features extracted from 2D signature images. A hybrid wavelet transform, denoted as HWT-1 with a size of 256, is formed through the Kronecker product of two orthogonal transforms, such as DCT, DHT, Haar, Hadamard, and Kekre, each with sizes 4 and 64. This HWT facilitates signal analysis at both global and local levels, akin to traditional wavelet transforms. Specifically, HWT-1 processes the 256 samples of online handwritten signatures, yielding 128 samples that constitute the feature vectors for signature verification and forgery detection. These feature vectors are then inputted into the Left-Right and Ergodic Hidden Markov Model (HMM) classifiers for further analysis. The HMMs are trained using 10 randomly selected genuine signature samples and subsequently tested with the remaining 10 genuine signatures and 20 forged signatures from the SVC 2004 signature database, repeating this process 20 times to compute average values. Among all possible combinations of HWT-1 utilizing DCT, DHT, Haar, Hadamard, and Kekre transforms for the Left-Right HMM model, the combination of DCT 4 and Haar 64 demonstrates the best performance with a False Rejection Rate (FRR) and False Acceptance Rate (FAR) of 1.05% and 0.99%, respectively, at state 3. Similarly, considering all feasible combinations of HWT-1 for the Ergodic HMM model, the combination of DCT 4 and Kekre 64 yields the optimal performance with an FRR and FAR of 1.24% and 1.33%, respectively, at state 3.

DWSCNN Online Signature Verification Algorithm Based on CAE-MV Feature Dimensionality Reduction

DWSCNN Online Signature Verification Algorithm Based on CAE-MV Feature Dimensionality Reduction

Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

Online Handwritten Signature Verification Method Based on Uni-Feature Correlation Coefficient between Signatures

Online handwritten signature verification is a crucial direction of research in the field of biometric recognition. Recently, many studies concerning online signature verification have attempted to improve performance using multi-feature fusion. However, few studies have provided the rationale for selecting a certain uni-feature to be fused, and few studies have investigated the contributions of a certain uni-feature in the multi-feature fusion process. This lack of research makes it challenging for future researchers in related fields to gain inspiration. Therefore, we use the uni-feature as the research object. In this paper, the uni-feature is one of the X and Y coordinates of the signature trajectory point, pen pressure, pen tilt, and pen azimuth feature. Aiming to solve the unequal length of feature vectors and the low accuracy of signature verification when using uni-features, we innovatively introduced the idea of correlation analysis and proposed a dynamic signature verification method based on the correlation coefficient of uni-features. Firstly, an alignment method of two feature vector lengths was proposed. Secondly, the correlation coefficient calculation formula was determined by analyzing the distribution type of the feature data, and then the correlation coefficient of the same uni-feature between the genuine signatures or between the genuine and forged signatures was calculated. Finally, the signature was verified by introducing a Gaussian density function model and combining it with the signature verification discrimination threshold. Experimental results showed that the proposed method could improve the performance of dynamic signature verification based on uni-features. In addition, the pen pressure feature had the best signature verification performance, with the highest signature verification accuracy of 93.46% on the SVC 2004 dataset.

Systematic evaluation of pre-processing approaches in online signature verification

Online signature verification is a currently evolving field. Numerous verification approaches are proposed each year that are claimed to be capable of delivering improved results in some aspects over others. These systems are usually based on the experiences of the author(s), and the results are often limited to single databases. These factors make it difficult to compare or even reproduce the results. The lack of negative results and evaluation of design choices make the creation of new, improved online verification systems difficult. In this work, we addressed this problem by conducting a systematic evaluation of the most common pre-processing techniques used in conjunction with dynamic time warping-based classification. We evaluated six different pre-processing methods on five databases and devoted over 30 000 computing hours to test 211 680 different verifier configurations. We identified several dead ends, narrowed down the design choices to the most promising configurations, and introduced a simple yet competitive configuration. To aid the research community, we have made all our results, data, and source code public. Our results may have broad implications for the signature verification field as they provide definitive answers for some questions that were previously addressed only through intuition or limited experimentation.

FBN: Federated Bert Network with client-server architecture for cross-lingual signature verification

Online signature verification has a great challenge due to the poor performance of deep learning techniques on cross-lingual datasets under privacy constraints. In this paper, we propose a novel Federated Bert Network (FBN) by embedding the Bidirectional Encoder Representations from Transformers (Bert) into a Federated Learning (FL) framework with client-server architecture. A new Length Alignment Algorithm is employed to unify the signature pairs’ sequence length, and the input representations are fed into the different clients to complete the independent learning of local-models. In addition, the server (coordinator) uses the improved Federated Average Algorithm with Reward-Punishment Mechanism (FedAvgRP) to aggregate these local-models and further generate a global-model. After multiple iterations, the optimal model can be obtained and cross-tested on four datasets (SVC 2004, MCYT-330, BioecurID, and Ours) with skilled forged (random forged) EERs of 7.65% (4.76%), 10.73% (8.46%), 10.09% (7.13%), and 8.28% (5.74%), respectively, far higher than that of the independent learning of state-of-the-art methods. Compared with the domain adaptation and improved FL models, our FBN model performs best in random and skilled forgery scenarios. Moreover, the FedAvgRP algorithm helps our model maintain high performance in the face of data attacks.

Offline Signature Verification Using Image Processing

A person’s signature is merely a handwritten sign that closely resembles his/her name, frequently stylized and distinctive, and that expresses the person’s identity, intent, and consent. Two types of verifications are present. They are online signature verification and offline signature verification. Generally, Offline Signature verification is less efficient and slower process compare to online verification when come to the situation having larger number of documents and files to verify with in less time. Over the years, many researchers have developed so many methods for signature verifications to help the people or organizations to find whether the signature of a particular person is forged or genuine. To overcome this problems; In this paper we introduced a simple method to improve the verification of the signature in Image Processing using Convolution Neural Networks(CNN). Signature Verification it is used to authenticate various kinds of documents, including cheques, draughts, certificates, approvals, letters, and other legal ones, such verification is crucial for preventing document forgery and falsification. Previously, to verify a signature, it was manually checked against copies of real signatures. This straightforward approach might not be sufficient given that forgery and signature fraud techniques are becoming more sophisticated as a result of improving technology.

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.

Deep attentive time warping

Similarity measures for time series are important problems for time series classification. To handle the nonlinear time distortions, Dynamic Time Warping (DTW) has been widely used. However, DTW is not learnable and suffers from a trade-off between robustness against time distortion and discriminative power. In this paper, we propose a neural network model for task-adaptive time warping. Specifically, we use the attention model, called the bipartite attention model, to develop an explicit time warping mechanism with greater distortion invariance. Unlike other learnable models using DTW for warping, our model predicts all local correspondences between two time series and is trained based on metric learning, which enables it to learn the optimal data-dependent warping for the target task. We also propose to induce pre-training of our model by DTW to improve the discriminative power. Extensive experiments demonstrate the superior effectiveness of our model over DTW and its state-of-the-art performance in online signature verification.

Impact of augmentation methods in online signature verification

The aim of this paper is to investigate the impact of selected data augmentation techniques on the learning performance of neural networks for dynamic signature verification. The paper investigates selected data augmentation techniques in deep learning for verification purpose of dynamic signature. Two neural networks were used as classifiers: MLP and LSTM-FCN. Investigation of five selected augmentation methods and experiments were performed on the open source signature database SVC2004. The authors tested both classifiers without augmentation and then with data augmentation for three extensions of the learning set and three sizes of the user database. They presented the results of the experiments in tabular form for each augmentation method. The results were compared with the existing dynamic signature verification methods and given in the paper.