Signature Verification Problem Research Articles

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.

TransOSV: Offline Signature Verification with Transformers

Signature verification is a frequently-used forensics technology in numerous safety-critical situations. Although convolutional neural networks (CNNs) have made significant advancements in the field of signature verification, their reliance on local neighborhood operations poses limitations in capturing the global contextual relationships among signature strokes. To overcome this weakness, in this paper, we propose a novel holistic-part unified model named TransOSV based on the vision transformer framework to solve offline signature verification problem. The signature images are first encoded into patch sequences by the proposed transformer-based holistic encoder to learn the global signature representation. Second, considering the subtle local difference between the genuine signature and forged signature, we design a contrast based part decoder along with a sparsity loss, which are utilized to learn the discriminative part features. With the learned holistic features and part features, the proposed model is optimized by the contrast loss function. To reduce the influence of sample imbalance, we also formulate a new focal contrast loss function. Furthermore, we conduct the proposed model to learn signature representations for writer-dependent signature verification task. The experimental results demonstrate the potential of the proposed TransOSV model for both writer-independent and writer-dependent signature verification tasks, achieving remarkable performance improvements and competitive results on four widely-used offline signature datasets.

2C2S: A two-channel and two-stream transformer based framework for offline signature verification

Recently, with the outstanding performance of the transformer in NLP, approaches that employ the transformer to address vision problem is becoming a research focus. However, transformer-based research rarely focuses on signature verification. To fill this gap, this paper proposes a two-channel and two-stream transformer approach (2C2S) to cope with the signature verification problem. 2C2S is composed of original and central streams. The original stream receives the original signature pair as input, and the central stream receives the signature pair generated by cropping the central at the original pair as input. In order to establish the associations among feature channels, a squeeze-and-excitation operation is applied between two standard Swin Transformer blocks. Moreover, an up-sampling enhancement module directly steers the model to focus on useful information. The verification accuracy of 2C2S on SUES-SiG and several publically available datasets: CEDAR, BHSig-B, and BHSig-H, reaches 93.25%, 90.68%, 100%, and 72.22%, respectively. Extensive experiments illustrate that the proposed framework is competitive with the existing techniques for offline handwritten signature verification.

Online handwritten signature verification via network analysis

In this paper, a model for online handwritten signature verification via networks analysis is proposed, in which centrality metrics are used as predictive characteristics. Supervised learning methods were used to classify the veracity of the signatures. The study uses the MCYT-100 and SVC2004 task2 signature databases, which are widely used in signature verification problems. In the framework, signatures were considered as nodes of a complex network in which the existing connection relationship between two signatures is measured through Pearson correlations between time series of signature coordinates that exceed a threshold of comparison. The attributes for the model were topological measures of centralities of the networks and the entropy of the time series signature. Some metrics are used to measure classification errors, including the False Rejection Rate (FRR) which identifies the percentage of true signatures that are rejected by the model, the False Acceptance Rate (FAR) as the percentage of false signatures that are accepted by the learning engine and the Average Error Rate (AER) as the average error. In this framework, we obtained a false positive rate of 6.19%, a false negative rate of 6.39% and an average error of approximately 6.28%. This result is close to ones obtained by state-of-the-art models for this problem, showing that the use of complex networks for online handwritten signature verification is a promising venue of research.

Offline handwritten signature verification using various Machine Learning Algorithms

In today’s world it is necessary to protect one’s authenticity in order to ensure the protection of personal information that only the authenticate credentials of a person can have access to. Nowadays there is an increase in number of malpractices like signature forgery to access the important information of a person. To encounter signature verification problem, there have been a number of advances in verifying the authenticity of signature using various techniques including Machine Learning and Deep Learning. This paper introduces a novel approach to verify the signatures using difference of gaussian filtering technique, gray level co-occurrence matrix feature extraction technique, principle component analysis and kernel principal component analysis associated with various machine learning algorithms. The publicly available Kaggle offline handwritten signature dataset is used for training. This article compares the accuracy of the dataset on various machine learning algorithms. After training datasets the lowest accuracy achieved is 56.66% for Naive Bayes algorithm. The highest accuracy achieved is 82% for K-Nearest Neighbour (KNN) and 81.66% for Random Forest using principle components and kernel principle components of the dataset.

Deep learning-based data augmentation method and signature verification system for offline handwritten signature

Offline handwritten signature verification is a challenging pattern recognition task. One of the most significant limitations of the handwritten signature verification problem is inadequate data for training phases. Due to this limitation, deep learning methods that have obtained the state-of-the-art results in many areas achieve quite unsuccessful results when applied to signature verification. In this study, a new use of Cycle-GAN is proposed as a data augmentation method to address the inadequate data problem on signature verification. We also propose a novel signature verification system based on Caps-Net. The proposed data augmentation method is tested on four different convolutional neural network (CNN) methods, VGG16, VGG19, ResNet50, and DenseNet121, which are widely used in the literature. The method has provided a significant contribution to all mentioned CNN methods’ success. The proposed data augmentation method has the best effect on the DenseNet121. We also tested our data augmentation method with the proposed signature verification system on two widely used databases: GPDS and MCYT. Compared to other studies, our verification system achieved the state-of-the-art results on MCYT database, while it reached the second-best verification result on GPDS.

A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification

High number of writers, small number of training samples per writer with high intra-class variability and heavily imbalanced class distributions are among the challenges and difficulties of the offline Handwritten Signature Verification (HSV) problem. A good alternative to tackle these issues is to use a writer-independent (WI) framework. In WI systems, a single model is trained to perform signature verification for all writers from a dissimilarity space generated by the dichotomy transformation. Among the advantages of this framework is its scalability to deal with some of these challenges and its ease in managing new writers, and hence of being used in a transfer learning context. In this work, we present a white-box analysis of this approach highlighting how it handles the challenges, the dynamic selection of references through fusion function, and its application for transfer learning. All the analyses are carried out at the instance level using the instance hardness (IH) measure. The experimental results show that, using the IH analysis, we were able to characterize “good” and “bad” quality skilled forgeries as well as the frontier region between positive and negative samples. This enables futures investigations on methods for improving discrimination between genuine signatures and skilled forgeries by considering these characterizations.

Off-line handwritten signature verification using compositional synthetic generation of signatures and Siamese Neural Networks

In this work, we propose the use of Siamese Neural Networks to help solve the off-line handwritten signature verification problem with random forgeries in a writer-independent context. Our proposed solution can be used on new signers without the need for any additional training. Also, we have analyzed three types of synthetic data to increase the amount of samples and the variability needed for training deep neural networks: augmented data samples from GAVAB dataset, a proposal of compositional synthetic signature generation from shape primitives and the GPDSSynthetic dataset. The first two approaches are “on-demand” generators and they can be used during the training stage to produce a potentially infinite number of synthetic signatures. In our approach, we initially trained Siamese Neural Networks using signatures from GAVAB dataset and different combinations of synthetic data. The best verification results were obtained when combining original and synthetic signatures for training. Additionally, we tested our approach on the GPSSynthetic, MCYT, SigComp11 and CEDAR datasets demonstrating the generalization capabilities of our proposal.

Investigating the Common Authorship of Signatures by Off-Line Automatic Signature Verification Without the Use of Reference Signatures

In automatic signature verification, questioned specimens are usually compared with reference signatures. In writer-dependent schemes, a number of reference signatures are required to build up the individual signer model while a writer-independent system requires a set of reference signatures from several signers to develop the model of the system. This paper addresses the problem of automatic signature verification when no reference signatures are available. The scenario we explore consists of a set of signatures, which could be signed by the same author or by multiple signers. As such, we discuss three methods which estimate automatically the common authorship of a set of off-line signatures. The first method develops a score similarity matrix, worked out with the assistance of duplicated signatures; the second uses a feature-distance matrix for each pair of signatures; and the last method introduces pre-classification based on the complexity of each signature. Publicly available signatures were used in the experiments, which gave encouraging results. As a baseline for the performance obtained by our approaches, we carried out a visual Turing Test where forensic and non-forensic human volunteers, carrying out the same task, performed less well than the automatic schemes.

Recurrent Adaptation Networks for Online Signature Verification

Online signature verification remains a challenging task owing to large intra-individual variability. To tackle this problem, in this paper, we propose to use recurrent neural networks (RNN) for representation learning in the dynamic time warping framework. Metric-based loss functions are designed explicitly to minimize intra-individual variability and enhance inter-individual variability and to guide the RNN in learning discriminative representations for online signatures. An RNN variant-gated auto regressive units-is proposed and shows a better generalization performance in our framework. Furthermore, we interpret the online signature verification problem as a meta-learning problem: one client is considered as one task, therefore, different clients compose the task space. Based on this formulation, we design an end-to-end trainable meta-layer that learns to adapt to different clients, allowing fast adaptation to new clients in the test stage. In addition, a new descriptor-the length-normalized path signature-is proposed to describe online signatures. Our proposed system achieves a state-of-the-art performance on three benchmark datasets, namely, MCYT-100, Mobisig, and e-BioSign.

Chinese Handwriting Identification Method Based on Keyword Extraction

Text-independent handwriting identification methods require that features such as texture are extracted from lengthy document image; while text-dependent handwriting identification methods require that the contents of the documents being compared are identical. In order to overcome these confinements, this paper presents a novel Chinese handwriting identification technique. First, Chinese characters are segmented from handwriting document, then keywords are extracted based on matching and voting of local features of character. Then the same-content keywords are used to build training sets, and these training sets of two documents are compared. Because the keywords are similar to signature, the handwriting identification problem is transformed into signature verification problem. Experiments on HIT-MW, HIT-SW and CASIA show this method outperforms many text-independent handwriting identification methods.

On the Exploration of Information From the DTW Cost Matrix for Online Signature Verification.

This paper explores the utility of information derived from the dynamic time warping (DTW) cost matrix for the problem of online signature verification. The prior works in literature primarily utilize only the DTW scores to authenticate a test signature. To the best of our knowledge, the characteristics of the warping path (used for the alignment) in the cost matrix is hardly exploited for verification of online signatures. Accordingly, we devise a score that utilizes the information from the cost matrix and warping path alignments. We subsequently consider its fusion (using a sum rule combiner) with the DTW score for authenticating the veracity of a test signature. In addition, a minor modification is suggested with regards to the set of features employed for matching the signatures. We introduce a spacing parameter for feature extraction and demonstrate its applicability in increasing the separation between the distribution of genuine and forgery signatures for an user. Our method has been tested on two publicly available online signature databases namely the SVC-2004 Task 2 and MCYT-100. We report reduction in error rates over the traditional DTW framework.

Examination of Simulated Forgery and its Investigation Protocol

Every day, we may face signature verification problem directly or indirectly whether it is in a banking transaction or signing a credit card transaction or authenticating a legal document. Signature identification and the questions of disguise, forgery and genuineness account for a significant proportion of a document examiner's case load. The development and examination, of a personal signature follows almost all of the concepts relating to handwriting. General indications of non-genuineness may include blunt starts and stops, pen lifts, hesitations, tremor, speed, pressure and patching. Forgery is an illegal modification or reproduction of an image, document signature, legal tender or any other means of recording information. Now-a-days, forgery detection is very important for real world events. In the present study 3 specimen of signature were collected from 50 individuals and were simulated for the examination of simulated forgery and its investigation protocol with Video Spectral Comparator.

Off-line Signature Recognition Using Weightless Neural Network and Feature Extraction

The problem of automatic signature recognition and verification has been extensively investigated due to the vitality of this field of research. Handwritten signatures are broadly used in daily life as a secure way for personal identification. In this paper a novel approach is proposed for handwritten signature recognition in an off-line environment based on Weightless Neural Network (WNN) and feature extraction. This type of neural networks (NN) is characterized by its simplicity in design and implementation. Whereas no weights, transfer functions and multipliers are required. Implementing the WNN needs only Random Access Memory (RAM) slices. Moreover, the whole process of training can be accomplished with few numbers of training samples and by presenting them once to the neural network. Employing the proposed approach in signature recognition area yields promising results with rates of 99.67% and 99.55% for recognition of signatures that the network has trained on and rejection of signatures that the network .has not trained on, respectively.

An Offline Signature Verification using Adaptive Resonance Theory 1 (ART1)

signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. We have designed offline signature verification and recognition system (SVRS) using Adaptive Resonance Theory-1(ART 1). In this paper a standard database of 250 signatures is used for calculating the performance of SVRS. The training of our system is done using ART-1 that uses global features as input vector and the verification and recognition phase uses a two step process. In first step, the input vector is matched with stored reference vector which was used as training set & in second step cluster formation takes place. If our given pattern matches with the stored pattern, it is accepted otherwise new cluster is formed. The presented approach achieved a classification ratio of 97.9% .The false acceptance rate (FAR) and false rejection rate (FRR) for given sample signatures is 2.7% and 3.9%.

A Study on Handwritten Signature

signature verification is a behavioral biometric. Every day, we may face signature verification problem directly or indirectly whether it is in a banking transaction or signing a credit card transaction or authenticating a legal document. In order to solve this problem, during the last few decades, research has been going on with different approaches to introduce an efficient signature verification and identification system. This paper presents some basic concepts of signature and also explores on different approaches for verification.

On-line signature verification based on spatio-temporal correlation

We address the problem of on-line signature representation and verification. A novel strategy is proposed in this paper by combining texture based image analysis and spatio-temporal representation. Firstly, a correlation based method is proposed to describe spatio-temporal information between sampling points, which is then converted to traditional 2D intensity images. Secondly, abundant texture analysis methods are adopted to construct effective features for high accuracy verification. Furthermore, a template selection strategy based on intra-class variations is presented to further enhance the performance of signature verification. Extensive experiments are conducted on the SVC2004 database and experimental results demonstrate the inspiring performance of our proposed methods.

A Novel Approach to Dynamic Signature Verification Using Sensor-Based Data glove

Data glove is a new dimension in the field of virtual reality environments, initially designed to satisfy the stringent requirements of modern motion capture and animation professionals. In this study we try to shift the implementation of data glove from motion animation towards signature verification problem, making use of the offered multiple degrees of freedom for each finger and for the hand as well. We used an SVD-based technique to extract the feature values of different sensors' locating on corresponding fingers in the signing process and evaluated the results for writer authentication. The technique is tested with large number of authentic and forgery signatures using data gloves with 14, 5 and 4 sensor and shows a significant level of accuracy with 2.46~5.0% of EER.

Fuzzy shape-memory snakes for the automatic off-line signature verification problem

This paper introduces an adapted fuzzy snake approach for efficiently solving some of the practical constraints in the off-line signature verification problem. Our method is called fuzzy shape-memory snakes due to its resemblance to shape-memory alloys, which are metals that in high-temperature conditions can remember their original shape. In our approach, the snake also “remembers” its geometry during its iterative adjustment to a test signature. Off-line signature verification aims to establish the degree of genuineness of a given test signature when compared to a reference signature. Due to the shape and size variability in signatures of the same subject, a system with tolerance to imprecision and also with some “memory” of its initial configured shape, would be very useful for this complex verification problem. To our knowledge, snakes and other active contour models have not been previosly applied to the offline signature verification problem. We consider that they could be properly adapted to be useful for this task. Consequently, we have developed a fuzzy snake framework for signature verification which takes into account some practical constraints of this problem when applied to bank checks. Over other signature verification systems, our approach has the advantage of using only one training signature per person. We introduce the fuzziness for the considered signature verification problem in a double direction. First, when iteratively adjusting a shape-memory snake (which is obtained from the training signature) to a considered test signature. Second, when measuring the similarity degree between the snake and the test signature after the adjustment (or verification task) using a Takagi–Sugeno fuzzy inference system, which is trained with three signature features (coincidence, distance and energy) provided by the adjustment. Some advantages of our approach are that: some involved parameters in the internal (shape) snake energy are now eliminated, and a more efficient and natural snake adjustment to the test signature is achieved. This paper also provides a study of the biometric classification errors when comparing our off-line signature verification approach to other non-fuzzy ones using the same signature database.

SVD-BASED SIGNATURE VERIFICATION TECHNIQUE USING DATA GLOVE

Data glove is a new dimension in the field of virtual reality environments, initially designed to satisfy the stringent requirements of modern motion capture and animation professionals. In this paper, we try to shift the implementation of data glove from motion animation towards signature verification problem, making use of the offered multiple degrees of freedom for each finger and for the hand as well. The proposed technique is based on the Singular Value Decomposition (SVD) in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, and thus account for most of the variation in the original data, so the effective dimensionality of the data can be reduced. Having identified data glove signature through its rth principal subspace, the authenticity can then be obtained by calculating the angles between the different subspaces. The SVD-signature verification technique is tested with large number of authentic and forged signatures, showing remarkable level of accuracy in finding the similarities between genuine samples as well as those differentiated between genuine-forgery trials.