On-line Signature Verification based on Pen Inclination and Pressure Information

In this paper, the features that have personal characteristic using pen inclination and pressure information are discussed. Forging a pen inclination and pressure information is difficult because it is not visible. Four features using invisible information are proposed and their characteristics are discussed. Proposed features calculated by physical vector analysis are verified by SVC2004 database using DP matching algorithm. As a result, the new feature named Down improves the recognition rate and reliability. Average of correct verification rate is 94.57 % and variance is 0.667.DOI:http://dx.doi.org/10.11591/ijece.v2i4.47

Incorporating signature verification on handheld devices with user-dependent Hidden Markov Models

Proceedings of the International Conference on Frontiers in Hadwriting Recognition (ICFHR 2008)A dynamic signature verification system based on Hidden Markov Models is presented. For each user model, the number of states and Gaussian mixtures of the Hidden Markov Model is automatically set in order to optimize the verification performance. By introducing this userdependent structure in the statistical modeling of signatures, the system error rate is significantly decreased in the challenging scenario of dynamic signature verification on handheld devices. Experimental results are given on a subset of the recently acquired BIOSECURE multimodal database, using signatures captured with a PDAThis work has been supported by the Spanish Ministry of Education under project TEC2006-13141-C03-03

DWT Domain On-line Signature Verification Using Pen-movement Vector

We examine a pen-movement vector parameter to reduce the computational complexity in the on-line signature verification method based on DiscreteWavelet Transform (DWT) and adaptive signal processing. The pen-movement vector is a time-varying signal which is derived from pen-position parameters and is decomposed into sub-band signals by using the DWT. Individual features are extracted as high frequency components in sub-bands. Verification is achieved in each sub-band by using the adaptive signal processing. Total decision for verification is done by combining multiple verification results. Experimental results show that the verification rate using the pen-movement vector parameter is equivalent to that of our conventional method which utilizes the pen-position parameter although computational complexity is reduced to half of that of the conventional method

DWT Domain Multi-matcher On-line Signature Verification System

This paper presents a multi-matcher on-line signature verification system which fuses the verification scores in pen-position parameter and pen-movement angle parameter at decision level. Features of pen-position and pen-movement angle are extracted by the sub-band decomposition using the Discrete Wavelet Transform (DWT). In the pen-position, high frequency sub-band signals are considered as individual features to enhance the difference between a genuine signature and its forgery. On the other hand, low frequency sub-band signals are utilized as the features for suppressing the intra-class variation in the penmovement angle. Verification is achieved by the adaptive signal processing using the extracted features. Verification scores in the pen-position and the pen-movement angle are integrated by using a weighted sum rule to make total decision. Experimental results show that fusion of pen-position and pen-movement angle can improve verification performance

Validation of dynamic signature for identity verification

Machine based identity validation is extremely important to determine the authenticity of documents, for financial transactions, and for e-communication. Recent explosion of frauds have demonstrated the ineffectiveness of password, personal identification numbers and biometrics. This thesis presents a signature verification technique which is inexpensive, user friendly, robust against impostors and is reliable, and insensitive to factors such as users’ exposure to emotional stimuli. This work has addressed three important issues which are: • The selection of appropriate features for dynamic and static signatures. • The suitable classifier for classification of the features. • The impact of emotional stimuli on the natural handwriting and signatures of the subjects. The thesis reports a comparison of the dynamic and static signatures and demonstrates that while the dynamic signature technique has a small increase in the rejection of the authentic user (92% compared with 94%), the system is far more discerning regarding the acceptance of the impostors (1% compared with 21%). The work also demonstrates that the use of ’unknown’ as a class reduces the rejection to zero, by putting those into a class who would be asked to repeat the experiment. This thesis has also studied the impact of emotional stimuli on peoples’ handwriting and signatures and has determined that while the signatures are insensitive to these stimuli, the handwriting is affected by these stimuli. This outcome may be of importance for people who conduct graphology analysis on people because this suggests that while general handwriting is affected by short term emotional changes of people, signatures are a more robust indicator of the person and hence their personality

Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health

Online handwritten analysis presents many applications in e-security, signature biometrics being the most popular but not the only one. Handwriting analysis also has an important set of applications in e-health. Both kinds of applications (e-security and e-health) have some unsolved questions and relations among them that should be addressed in the next years. We summarize the state of the art and applications based on handwriting signals. Later on, we focus on the main achievements and challenges that should be addressed by the scientific community, providing a guide for future research. Among all the points discussed in this article, we remark the importance of considering security, health, and metadata from a joint perspective. This is especially critical due to the risks inherent when using these behavioral signals

Verificaciónn de firma y gráficos manuscritos: Características discriminantes y nuevos escenarios de aplicación biométrica

Tesis doctoral inédita leída en la Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: Febrero 2015The proliferation of handheld devices such as smartphones and tablets brings a new scenario for biometric authentication, and in particular to automatic signature verification. Research on signature verification has been traditionally carried out using signatures acquired on digitizing tablets or Tablet-PCs. This PhD Thesis addresses the problem of user authentication on handled devices using handwritten signatures and graphical passwords based on free-form doodles, as well as the effects of biometric aging on signatures. The Thesis pretends to analyze: (i) which are the effects of mobile conditions on signature and doodle verification, (ii) which are the most distinctive features in mobile conditions, extracted from the pen or fingertip trajectory, (iii) how do different similarity computation (i.e. matching) algorithms behave with signatures and graphical passwords captured on mobile conditions, and (iv) what is the impact of aging on signature features and verification performance. Two novel datasets have been presented in this Thesis. A database containing free-form graphical passwords drawn with the fingertip on a smartphone is described. It is the first publicly available graphical password database to the extent of our knowledge. A dataset containing signatures from users captured over a period 15 months is also presented, aimed towards the study of biometric aging. State-of-the-art local and global matching algorithms are used, namely Hidden Markov Models, Gaussian Mixture Models, Dynamic Time Warping and distance-based classifiers. A large proportion of features presented in the research literature is considered in this Thesis. The experimental contribution of this Thesis is divided in three main topics: signature verification on handheld devices, the effects of aging on signature verification, and free-form graphical password-based authentication. First, regarding signature verification in mobile conditions, we use a database captured both on a handheld device and digitizing tablet in an office-like scenario. We analyze the discriminative power of both global and local features using discriminant analysis and feature selection techniques. The effects of the lack of pen-up trajectories on handheld devices (when the stylus tip is not in contact with the screen) are also studied. We then analyze the effects of biometric aging on the signature trait. Using three different matching algorithms, Hidden Markov Models (HMM), Dynamic Time Warping (DTW), and distance-based classifiers, the impact in verification performance is studied. We also study the effects of aging on individual users and individual signature features. Template update techniques are analyzed as a way of mitigating the negative impact of aging. Regarding graphical passwords, the DooDB graphical password database is first presented. A statistical analysis is performed comparing the database samples (free-form doodles and simplified signatures) with handwritten signatures. The sample variability (inter-user, intra-user and inter-session) is also analyzed, as well as the learning curve for each kind of trait. Benchmark results are also reported using state of the art classifiers. Graphical password verification is afterwards studied using features and matching algorithms from the signature verification state of the art. Feature selection is also performed and the resulting feature sets are analyzed. The main contributions of this work can be summarized as follows. A thorough analysis of individual feature performance has been carried out, both for global and local features and on signatures acquired using pen tablets and handheld devices. We have found which individual features are the most robust and which have very low discriminative potential (pen inclination and pressure among others). It has been found that feature selection increases verification performance dramatically, from example from ERRs (Equal Error Rates) over 30% using all available local features, in the case of handheld devices and skilled forgeries, to rates below 20% after feature selection. We study the impact of the lack of trajectory information when the pen tip is not in contact with the acquisition device surface (which happens when touchscreens are used for signature acquisitions), and we have found that the lack of pen-up trajectories negatively affects verification performance. As an example, the EER for the local system increases from 9.3% to 12.1% against skilled forgeries when pen-up trajectories are not available. We study the effects of biometric aging on signature verification and study a number of ways to compensate the observed performance degradation. It is found that aging does not affect equally all the users in the database and that features related to signature dynamics are more degraded than static features. Comparing the performance using test signatures from the first months with the last months, a variable effect of aging on the EER against random forgeries is observed in the three systems that are evaluated, from 0.0% to 0.5% in the DTW system, from 1.0% to 5.0% in the distance-based system using global features, and from 3.2% to 27.8% in the HMM system. A new graphical password database has been acquired and made publicly available. Verification algorithms for finger-drawn graphical passwords and simplified signatures are compared and feature analysis is performed. We have found that inter-session variability has a highly negative impact on verification performance, but this can be mitigated performing feature selection and applying fusion of different matchers. It has also been found that some feature types are prevalent in the optimal feature vectors and that classifiers have a very different behavior against skilled and random forgeries. An EER of 3.4% and 22.1% against random and skilled forgeries is obtained for free-form doodles, which is a promising performance