Online Signature Verification Research Articles

Online signature verification based on string edit distance

Handwritten signatures are widely used and well-accepted biometrics for personal authentication. The accuracy of signature verification systems has significantly improved in the last decade, making it possible to rely on machines in particular cases or to support human experts. Yet, based on only few genuine references, signature verification is still a challenging task. The present paper provides a comprehensive comparison of two prominent string matching algorithms that can be readily used for signature verification. Moreover, it evaluates a recent cost model for string matching which turns out to be particularly well suited for the task of signature verification. On three benchmarking data sets, we show that this model outperforms the two standard models for string matching with statistical significance.

Online handwritten signature verification based on association of curvature and torsion feature with Hausdorff distance

The paper presents an efficient on-line signature verification method based on the dynamic features of a given signature. In the proposed approach, curvature and torsion feature are associated with Hausdorff distance measure which can be used in the verification process. In the feature extraction step, the signature trajectory is approximated as a spatial curve. A set of curvature and torsion value of extreme point is computed from both x coordinate, y coordinate and pressure feature so that the dimension of the curve is reduced. Therefore, a new composed signature feature is created for each person. For the obtained feature data, the most distinctive Hausdorff distance is further proposed to calculate the distances of the eight-dimensional feature vector between the test signature and corresponding template signatures for the verification of the test sample. Comprehensive experiments are implemented on three publicly available databases: the SVC2004, SUSIG and MCYT-100 database. A comparison of our results with some recent signature verification methods available in the literature is provided with equal error rate, and the results indicate that the proposed method would better recognize genuine signatures, random and skilled forgeries.

Template Matching Using Time-Series Averaging and DTW With Dependent Warping for Online Signature Verification

Online signature verification has been widely applied in biometrics and forensics. Due to the recent demand on high-speed systems in this era of big data, to simultaneously improve its performance and calculation complexity, this study focuses on a single-template strategy that uses dynamic time warping (DTW) with dependent warping for online signature verification, and attempts to construct a novel time-series averaging method called Euclidean barycenter-based DTW barycenter averaging (EB-DBA). Specifically, this study proposes a single-template strategy using a mean template created by the EB-DBA to achieve higher performance at lower calculation complexity for online signature verification. The method's discriminative power is enhanced upon the exploration of two DTW warping types, where it is found that the DTW with dependent warping exhibits better performance. The popular MCYT-100 dataset is utilized in the experiments, which confirms the effectiveness of the proposed method in simultaneously achieving lower error rate and lower calculation complexity, for online signature verification.

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.

Anthropomorphic Features for On-Line Signatures.

Many features have been proposed in on-line signature verification. Generally, these features rely on the position of the on-line signature samples and their dynamic properties, as recorded by a tablet. This paper proposes a novel feature space to describe efficiently on-line signatures. Since producing a signature requires a skeletal arm system and its associated muscles, the new feature space is based on characterizing the movement of the shoulder, the elbow and the wrist joints when signing. As this motion is not directly obtained from a digital tablet, the new features are calculated by means of a virtual skeletal arm (VSA) model, which simulates the architecture of a real arm and forearm. Specifically, the VSA motion is described by its 3D joint position and its joint angles. These anthropomorphic features are worked out from both pen position and orientation through the VSA forward and direct kinematic model. The anthropomorphic features' robustness is proved by achieving state-of-the-art performance with several verifiers and multiple benchmarks on third party signature databases, which were collected with different devices and in different languages and scripts.

Online handwritten signature verification using feature weighting algorithm relief

Online handwritten signatures are widely used as a reliable identity authentication technology in industries such as banking, insurance and hospitals. Most existing online handwritten signature verification schemes usually choose the same feature sets for different users while comparing different signatures, which may reduce the stability of the dynamic characteristics. To avoid this problem, we propose a novel writer-dependent online signature verification technique based on Relief. For each user, our verification scheme falls into two phases: the training phase and the test phase. In the training phase, we select a signature as the base signature from real signatures and construct two different kinds of signature pairs: the real signature pairs and the forged signature pairs. Each pair of real signatures consists of two real signatures, and each pair of forgeries is made up of a real signature and a forgery. A group of combined features are defined for each pair of signatures. Then, we select more stable combined features by means of the Relief algorithm and compute a matrix used as a matching template, which contains the value of each combined feature for each pair of signatures. In the test phase, the classification of the pair of signatures composed of the test signature and the base reference signature is determined by the K-nearest neighbor. Experiments are conducted on our own database and SVC2004, and the results indicate that the method we propose here shows lower FAR and FRR than do traditional methods on the same databases.

Online signature verification modeled by stability oriented reference signatures

Signature verification is widely used in the fields of finance, access control and security. However, one of the biggest problems associated with signature verification is its instability. This instability may concern specific fragments of a signature or its entirety. The location of the unstable fragments in a signature depends on individual writing styles, which makes the signature verification more difficult, resulting in a negative impact on recognition effectiveness.In this paper the authors propose a new method of signature verification. The key stage of this approach is the determination of reference signature stability. The proposed signature stability measure, based on fuzzy set theory, is a biometric strategy that has never been used before. The fragments of one reference signature that differ from the corresponding fragments of the remaining reference signatures of the same person are treated as unstable fragments and will not be taken into consideration when comparing the reference sample with the signature being verified. The proposed method employs fuzzy sets to extract a signature’s stable fragments.To test the proposed method’s performance, seven different classifiers were used: PSO oriented, k-Nearest-Neighbor, Naive Bayes, Random Forest, RIDOR, Support Vector Machine and J48. Experiments conducted on the two independent datasets demonstrated that the method proposed here returns highly satisfactory results, outperforming the other state-of-the-art methods.

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.

Online signature verification by continuous wavelet transformation of speed signals

Despite the imitability of the signatures due to presence of numerous image processing programs, online verification systems could provide sufficient security for e-signatures. Recent developments in touchscreen technology and android programming also lead to utilization of hidden interfaces stealthily collecting the unique characteristics and storing the key features aside from geometrics. Therefore, we initially designed a signing interface for touchscreens which stealthily collects the precise coordinates while an individual is signing on the screen by fingertips. Even if the coordinate data is extracted as a matrix consisting of x and y values with corresponding time, the speed array is consequently calculated to investigate the higher frequency regions. The speed data processed by continuous wavelet transformations (CWT) to reveal the frequency information of the signing speed with respect to time information. The grayscale spectrograms created by wavelet transforms are converted into arrays for subsequent training session performed by support vector machines (SVM). The trained network successfully classified further attempts of the real and fake signatures with 1.67% false negative (FNR), 3.33% false positive rates (FPR) and 3.41% equal error rate (EER) for 120 signatures, even though the signature is totally public. For understanding the validity of the CWT and SVM running consecutively, the experiments are re-conducted for the signatures taken from SVC2004 and SUSIG public databases.

Online handwritten signature verification based on the most stable feature and partition

Existing methods for online signature verification are generally writer independent, as a common set of features is used for all writers during verification. In this paper, we propose a new method of online handwritten signature verification. Our approach is based on the writer dependent feature as well as writer dependent partition. The two decisions namely optimal feature suitable for a writer and a partition to be used for authenticating the writer, they are taken based on the error rate at the training phase. It is difficult for the forger to imitate the shape and dynamic characteristics of the signer at the same time. According to this feature, we propose to decompose signature trajectories depending upon pressure, velocity direction angle, and velocity information and perform verification on the most stable partition. Experimental results demonstrate superiority of our approach in online signature verification in comparison with other schemes.

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

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

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

Online Signature Verification Based on Stable Features Extracted Dynamically

Effective information extraction and optimal feature subspace selection have great effects on the performance of online signature verification. One of the difficulties about online signature verification is to extract and select effective features, which are stable in genuine but discriminative to distinguish forgeries. Different from other works, stability of spectral information inherent in signatures is analyzed in this paper, and stable spectral information is selected dynamically dependent on individuals to reconstruct the stable spectral features. In order to extract more effective spectral information, features are decomposed by wavelet packet with the optimal mother wavelet. To enhance the security level of online signature verification, discriminative capabilities of stable spectral features are analyzed by factorial experiment design. The optimal feature subspace is selected according to contribution rate. Furthermore, we proposed a simple and effective modified dynamic time warping (DTW) with signature curves constraint to solve the problem of heavy computation of DTW. Several experiments are carried out on open access database of MCYT_DB1 and SVC2004 task2 which consist of 6600 signatures from 140 individuals in total. Experiment results demonstrate the effectiveness and robustness of our proposed method.

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

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

Discriminative feature selection for on-line signature verification

On-line handwritten signatures are collected as real-time dynamical signals which are written on collective devices by users. Since individuals have different writing habits, consistent and discriminative features should be selected to distinguish genuine signatures from forged signatures. In this paper, two methods, which are based on full factorial experiment design and optimal orthogonal experiment design, are proposed for selecting discriminative features among candidates. To improve the robustness, consistency of feature is analyzed at first, and more consistent features are selected as candidates for discriminative feature selection. To reduce the influences of fluctuations caused by internal and external writing environments changes before verification, signatures are effectively aligned to their reference templates based on Gaussian mixture model. A modified dynamic time warping with signature curve constraint is presented for verification to improve the efficiency. Comprehensive experiments are implemented based on the data of the open access databases MCYT and SVC2004 Task2. Experimental results verify the effectiveness and robustness of our proposed methods.

VerSig: a new approach for online signature verification

This paper introduces, VerSig, a new proposed scheme for online signature verification. The proposed scheme is based on creation of a signature envelope by employing dynamic time warping method. This envelope provides the basis for decision of forged and authentic signatures. The scheme only uses basic features such as X, Y coordinates of the signature. A well known and standardized Japanese handwritten dataset (provided for ICDAR 2013 signature verification competition) is used to evaluate the performance of proposed method. Proposed method is compared with state of art methods and observed to offer significant improvements in terms of overall accuracy of prediction.

Online signature verification by spectrogram analysis

The concept of electronic signatures emerged decades ago, however they are still not prevalent due to lack of reliable infrastructure. Although the signatures are hard to perfectly imitate, it is simple with an image editing software to copy the original signature and paste on a document. On the other hand, technological developments of touchscreens may create a new era by utilizing simple interfaces which would be recording and validating the electronic signatures with biometric features. Therefore, in this paper, we propose a novel online signature analysis methodology for touchscreens that starts with signing an interface consisting of a signature silhouette. The frequency spectrum along the signing process is stealthily extracted and spectrograms are created by short-time Fourier transforms. Since the spectrograms are found as RGB images, providing valuable information about frequency vs time, grid histograms are formed by quantization for the real signature sample. Given the discrimination purposes, a fuzzified surface is designed for computing closeness of grid histograms.

Online Signature Verification: To What Extent Should a Classifier be Trusted in?

To select the best features to model the signatures is one of the major challenges in the field of online signature verification. To combine different feature sets, selected by different criteria, is a useful technique to address this problem. In this line, the analysis of different features and their discriminative power has been researchers’ main concern, paying less attention to the way in which the different kind of features are combined. Moreover, the fact that conflicting results may appear when several classifiers are being used, has rarely been taken into account. In this paper, a score level fusion scheme is proposed to combine three different and meaningful feature sets, viz., an automatically selected feature set, a feature set relevant to Forensic Handwriting Experts (FHEs), and a global feature set. The score level fusion is performed within the framework of the Belief Function Theory (BFT), in order to address the problem of the conflicting results appearing when multiple classifiers are being used. Two different models, namely, the Denoeux and the Appriou models, are used to embed the problem within this framework, where the fusion is performed resorting to two well-known combination rules, namely, the Dempster-Shafer (DS) and the Proportional Conflict Redistribution (PCR5) one. In order to analyze the robustness of the proposed score level fusion approach, the combination is performed for the same verification system using two different classification techniques, namely, Ramdon Forests (RF) and Support Vector Machines (SVM). Experimental results, on a publicly available database, show that the proposed score level fusion approach allows the system to have a very good trade-off between verification results and reliability.

Signature Verification Based on the Kinematic Theory of Rapid Human Movements

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