Handwritten Signature Verification Research Articles

MmSign: mmWave-based Few-Shot Online Handwritten Signature Verification

Handwritten signature verification has become one of the most important document authentication methods that are widely used in the financial, legal, and administrative sectors. Compared with offline methods based on static signature images, online handwritten signature verification methods are more reliable because of the temporary dynamic information (e.g., signing velocity, writing force, stroke order) that alleviates the risk of being forged. However, most existing online handwritten signature verification solutions are reliant on specific signing devices (e.g., customized pens or writing pads) and require extensive data collection during the registration phase, resulting in poor adaptability and applicability for new users. In this article, we propose mmSign, a millimeter wave (mmWave)–based online handwritten signature verification system, which enables accurate sensing of the user’s hand movements when signing through the superior sensing capability of mmWave. mmSign extracts the time-velocity feature maps from the captured mmWave signals by the carefully designed signal processing algorithms and then exploits a transformer-based verification model for signature verification. In addition, a novel meta-learning strategy with proposed task generation and data augmentation methods is introduced in mmSign to teach the verification model to learn effectively with limited samples, allowing our model to quickly adapt to new users. Extensive experiments show that mmSign is a robust, efficient, and secure handwritten signature verification system, achieving 84.07%, 87.31%, 91.12%, and 96.54% verification accuracy when 1, 3, 5, and 10 labeled signatures are available, respectively, while being resistant to common forgery attacks.

Handwritten Signature Verification System Using User-dependent Approach: A Comparative Study

Handwritten signature verification is a critical aspect of personal authentication in numerous sectors, including banking, legal, and access control systems. This paper provides a comprehensive investigation into writer-dependent signature verification techniques, spanning from traditional approaches such as Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) to cutting-edge deep learning architectures like Siamese and triplet networks. Through extensive experimentation and meticulous analysis conducted on benchmark datasets, we rigorously compare and evaluate the efficacy of these approaches. Our study reveals intriguing insights into the performance characteristics of different methodologies, shedding light on their strengths, weaknesses, and areas for improvement. Notably, we observe that while traditional feature-based methods excel in capturing discriminative information from signature images, deep learning architectures offer enhanced flexibility and adaptability, particularly in scenarios with large and diverse datasets. By combining the strengths of both paradigms, we demonstrate the potential for building robust and reliable signature verification systems capable of handling real-world challenges and variations. We highlight the importance of carefully curated datasets and well-designed network architectures in achieving optimal results, thereby providing valuable guidelines for practitioners and researchers in the field. In conclusion, this study contributes significant insights to the advancement of handwritten signature verification systems, emphasizing the importance of leveraging both traditional methodologies and modern deep learning techniques for building secure and dependable authentication mechanisms. By addressing key challenges and exploring novel approaches, our research aims to foster innovation and enhance the security measures associated with handwritten signature authentication in diverse applications. Keywords—Handwritten signature verification, Writer-dependent approach, Feature-based methods, Deep learning, Siamese networks, Triplet networks, Benchmark datasets, Performance evaluation, Authentication mechanisms, Security measures.

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.

Application of nonparametric quantifiers for online handwritten signature verification: A statistical learning approach

AbstractThis work explores the use of nonparametric quantifiers in the signature verification problem of handwritten signatures. We used the MCYT‐100 (MCYT Fingerprint subcorpus) database, widely used in signature verification problems. The discrete‐time sequence positions in the x ‐axis and y‐axis provided in the database are preprocessed, and time causal information based on nonparametric quantifiers such as entropy, complexity, Fisher information, and trend are employed. The study also proposes to evaluate these quantifiers with the time series obtained, applying the first and second derivatives of each sequence position to evaluate the dynamic behavior by looking at their velocity and acceleration regimes, respectively. The signatures in the MCYT‐100 database are classified via Logistic Regression, Support Vector Machines (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). The quantifiers were used as input features to train the classifiers. To assess the ability and impact of nonparametric quantifiers to distinguish forgery and genuine signatures, we used variable selection criteria, such as: information gain, analysis of variance, and variance inflation factor. The performance of classifiers was evaluated by measures of classification error such as specificity and area under the curve. The results show that the SVM and XGBoost classifiers present the best performance.

Off-line handwritten signature verification based on machine learning

This paper describes the results of recognizing handwritten signatures. For the experiments, the database of handwritten signatures BHSig260-Bengali, BHSig260-Hindi, CEDAR and TUIT was used. For classification, four options were used to reduce the signatures to sizes: 200×120, 250×150, 300×150 and 400×200 pixels. These images served as input for the proposed network architecture. As a result of testing the proposed approach, the average accuracy of correct classification of signatures on images of size 250×150 was achieved: for the CEDAR database it was 94.38%, for the BHSig260-Hindi database it was 95.63%, for the BHSig260-Bengali database it was 97.50% and for TUIT base is 90.04%.

Offline Handwriting Signature Verification: A Transfer Learning and Feature Selection Approach

Offline Handwriting Signature Verification: A Transfer Learning and Feature Selection Approach

MmHSV: In-Air Handwritten Signature Verification via Millimeter-Wave Radar

Electronic signatures are widely used in financial business, telecommuting, and identity authentication. Offline electronic signatures are vulnerable to copy or replay attacks. Contact-based online electronic signatures are limited by indirect contact such as handwriting pads and may threaten the health of users. Consider combining hand shape features and writing process features to form electronic signatures, the article proposes an in-air handwritten signature verification system with millimeter-wave(mmWave) radar, namely mmHSV. First, the biometrics of the handwritten signature process are modeled, and phase-dependent biometrics and behavioral features are extracted from the mmWave radar mixture signal. Secondly, a handwritten feature recognition network based on few-sample learning is presented to fuse multi-dimensional features and determine user legitimacy. Finally, mmHSV is implemented and evaluated with commercial mmWave devices in different scenarios and attack mode conditions. Experimental results show that the mmHSV can achieve accurate, efficient, robust and scalable handwritten signature verification. Area Under Curve (AUC) is 98.96%, False Acceptance Rate (FAR) is 5.1% at the fixed threshold, AUC is 97.79% for untrained users.

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.

Automatic Signature Verifier Using Gaussian Gated Recurrent Unit Neural Network

Handwritten signatures are one of the most extensively utilized biometrics used for authentication, and forgeries of this behavioral biometric are quite widespread. Biometric databases are also difficult to access for training purposes due to privacy issues. The efficiency of automated authentication systems has been severely harmed as a result of this. Verification of static handwritten signatures with high efficiency remains an open research problem to date. This paper proposes an innovative introselect median filter for preprocessing and a novel Gaussian gated recurrent unit neural network (2GRUNN) as a classifier for designing an automatic verifier for handwritten signatures. The proposed classifier has achieved an FPR of 1.82 and an FNR of 3.03. The efficacy of the proposed method has been compared with the various existing neural network-based verifiers.

Exploiting Deep Learning Techniques for the Verification of Handwritten Signatures

Biometric featuresare common measures of identity verification where signaturesarethe most used type. The digital technology has given birth to new ways of biometric identification, such as fingerprints, iris and face recognition,while dealing with handwritten signatures is still a challenging task, because handwritten signatures are more prone to forgery than other means of verification due to issues like computer error, insufficient datasets, and loss of information. This work aims to develop a system that takes a signature image as its input and determines whether the signature is genuine written by its author or forged by another individual. The system is based on a neural network algorithm called Convolutional Siamese Neural Networks, which is used for deep learning and computer vision as well as other machine learning tasks such as natural language processing and digital signal processing.A Contrastive Loss function which compares the Euclidean distance of the output feature vectors is used, and a writer-independent model is used for training and image classification. This work’s objective is toenhance the precision of signature verification and take it as a base for future work on signature verification and use it in user identification, fraud detection and prevention, and forensic investigation applications. The system can be applied in banking, government and private organizations, and forensic investigation for identity and document verification, impersonation and fraud detection and prevention, crime and judicial investigation, and passport verification

Distribution of local curvature values as a structural feature for off-line handwritten signature verification

In the paper, a new feature for describing a digital image of a handwritten signature based on the frequency distribution of the values of the local curvature of the signature contours, is proposed. The calculation of this feature on the binary image of a signature is described in detail. A normalized histogram of distributions of local curvature values for 40 bins is formed. The frequency values recorded as a 40-dimensional vector are called the local curvature code of the signature.During verification, the proximity of signature pairs is determined by correlation between curvature codes and LBP codes described by the authors in [23]. To perform the signature verification procedure, a two-dimensional feature space is constructed containing images of the proximity of signature pairs. When verifying a signature with N authentic signatures of the same person, N(N-1)/2 patterns of the proximity of pairs of genuine signatures and N images of pairs of proximity of the analyzed signature with genuine signatures are presented in the feature space. The Support Vector Machine (SVM) is used as a classifier.Experimental studies were carried out on digitized images of genuine and fake signatures from two databases. The accuracy of automatic verification of signatures on the publicly available CEDAR database was 99,77 % and on TUIT was 88,62 %.

FC-ResNet: A Multilingual Handwritten Signature Verification Model Using an Improved ResNet with CBAM

Offline signature verification is a widely used biometric method in finance, law, and administrative procedures. However, existing deep convolutional neural network models perform poorly on signature datasets that span different regions and ethnic people, while also suffering from problems such as large parameter counts and slow inference speeds. To address these issues, we propose an improved residual network model (FC-ResNet). This model introduces a convolutional block attention module into the classical residual network to adapt to the diversity and variability of signatures, while also compressing the model for lightweight deployment. Due to the lack of public, offline handwritten signature datasets for ethnic people, we collected a large-scale offline handwritten signature dataset, including genuine signatures and forged signatures in Chinese, Uyghur, Kazakh, and Kirgiz, totaling 38,400 images. Our FC-ResNet model achieved an accuracy of over 96% for each language in our self-built dataset, as well as accuracy rates of 96.21%, 98.42%, and 97.28% on the public datasets CEDAR, BHSig-B, and BHSig-H, respectively. Based on the above experimental results, our proposed model demonstrates great potential for both public and self-built signature datasets, while also exhibiting significant advantages in lightweight model deployment. We believe that this work can provide a feasible solution for ethnic people signature verification.

Multi-dilation Convolutional Neural Network for Automatic Handwritten Signature Verification

Multi-dilation Convolutional Neural Network for Automatic Handwritten Signature Verification

Research advanced in offline handwritten signature verification

Biometrics technology has penetrated into various fields of our lives that require authentication and verification, such as criminal investigation, check processing and legal procedures, which is a focus of debate in the scientific research community. As a biometric feature that is easy to obtain, verification of handwritten signatures has attracted great interest in the past several decades. Although the online handwritten signature verification (OnHSV) system can obtain more information and has a higher accuracy rate, the offline handwritten signature verification (OfHSV) system remains the focus of research, since most signatures we can obtain are offline. In this paper, according to the difference of steps of OfHSV, we classify and introduce the advanced research work of OfHSV from four aspects: datasets, preprocessing techniques, feature extraction methods, and neural network structures. Additionally, we introduced the background of signature verification system and the experimental results of existing technologies. Finally, we presented some notable challenges, leading the reader to current trends and future directions in this field.

A multi-dimensional review on handwritten signature verification: strengths and gaps

A multi-dimensional review on handwritten signature verification: strengths and gaps

High-Performance Embedded System for Offline Signature Verification Problem Using Machine Learning

This paper proposes a high-performance embedded system for offline Urdu handwritten signature verification. Though many signature datasets are publicly available in languages such as English, Latin, Chinese, Persian, Arabic, Hindi, and Bengali, no Urdu handwritten datasets were available in the literature. So, in this work, an Urdu handwritten signature dataset is created. The proposed embedded system is then used to distinguish genuine and forged signatures based on various features, such as length, pattern, and edges. The system consists of five steps: data acquisition, pre-processing, feature extraction, signature registration, and signature verification. A majority voting (MV) algorithm is used for improved performance and accuracy of the proposed embedded system. In feature extraction, an improved sinusoidal signal multiplied by a Gaussian function at a specific frequency and orientation is used as a 2D Gabor filter. The proposed framework is tested and compared with existing handwritten signature verification methods. Our test results show accuracies of 66.8% for ensemble, 86.34% for k-nearest neighbor (KNN), 93.31% for support vector machine (SVM), and 95.05% for convolutional neural network (CNN). After applying the majority voting algorithm, the overall accuracy can be improved to 95.13%, with a false acceptance rate (FAR) of 0.2% and a false rejection rate (FRR) of 41.29% on private dataset. To test the generalization ability of the proposed model, we also test it on a public dataset of English handwritten signatures and achieve an overall accuracy of 97.46%.

CNN with Batch Normalization Adjustment for Offline Hand-written Signature Genuine Verification

Signature genuine verifications of offline hand-written signatures are critical for preventing forgery and fraud. With the growth of protecting personal identity and preventing fraud, the demand for an automatic system for signature verification is high. The signature verification system is then studied by many researchers using various methods, especially deep learning-based methods. Hence, deep learning has a problem. Deep learning requires much training time for the data to obtain the best model accuracy result. Therefore, this paper proposed a CNN Batch Normalization, the CNN architectural adaptation model with a normalization batch number added, to obtain a CNN model optimization with high accuracy and less training time for offline hand-written signature verification. We compare CNN with our proposed model in the experiments. The research method in this study is data collection, pre-processing, and testing using our private signature dataset (collected by capturing signature images using a smartphone), which becomes the difficulties of our study because of the different lighting, media, and pen used to sign. Experiment results show that our model ranks first, with a training accuracy of 88.89%, an accuracy validation of 75.93%, and a testing accuracy of 84.84%—also, the result of 2638.63 s for the training time consumed with CPU usage. The model evaluation results show that our model has a smaller EER value; 2.583, with FAR = 0.333 and FRR = 4.833. Although the results of our proposed model are better than basic CNN, it is still low and overfitted. It has to be enhanced by better pre-processing steps using another augmentation method required to improve dataset quality.Â

A multi-task approach for contrastive learning of handwritten signature feature representations

In spite of recent advances in computer vision, the classic problem of offline handwritten signature verification still remains challenging. The signature verification task has a high intra-class variability because a given user often shows high variability between its samples. Besides, signature verification is harder in the presence of skilled forgeries. Recently, in order to tackle these challenges, the research community has investigated deep learning methods for learning feature representations of handwritten signatures. When mapping signatures to a feature space, it is desired to obtain dense clusters of signature’s representations, in order to deal with intra-class variability. Besides, not only dense clusters are required but also a larger separation between different user’s clusters in the feature space. Finally, it is also desired to move away feature representations of skilled forgeries in relation to the respective dense cluster of genuine representations. This last property is hard to achieve in the real-world scenario because skilled forgeries are not readily available during training. In this work, we hypothesize that such properties can be achieved by means of a multi-task framework for learning handwritten signature feature representations based on deep contrastive learning. The proposed framework is composed of two objective-specific tasks. The first task aims to map signature examples of the same user closer within the feature space, while separating the feature representations of signatures of different users. The second task aims to adjust the skilled forgeries representations by adopting contrastive losses with the ability to perform hard negative mining. Hard negatives are examples from different classes with some degree of similarity that can be applied for training. We evaluated models obtained with the proposed framework in terms of the equal error rate on GPDSsynthetic, CEDAR and MCYT-75 datasets in writer-dependent and writer-independent verification approaches. Using synthetic and real signature datasets, Friedman tests with Bonferroni–Dunn post hoc tests were performed to compare the proposed multi-task contrastive models against the popular SigNet model as a baseline. Experiments demonstrated an statistically significant improvement in signature verification with a multi-task contrastive model based on the Triplet loss. Implementation of the method is available for download at https://github.com/tallesbrito/contrastive_sigver.

基于特征选择和幻象残差网络的在线手写签名认证

基于特征选择和幻象残差网络的在线手写签名认证