Enhanced on-line signature verification based on skilled forgery detection using Sigma-LogNormal Features

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. M. Gomez-Barrero, J. Galbally, J. Fierrez, and J. Ortega-Garcia, "Enhanced on-line signature verification based on skilled forgery detection using Sigma-LogNormal Features", in International Conference on Biometrics, ICB 2015, 501-506One of the biggest challenges in on-line signature verification is the detection of skilled forgeries. In this paper, we propose a novel scheme, based on the Kinematic Theory of rapid human movements and its associated Sigma LogNormal model, to improve the performance of on-line signature verification systems. The approach combines the high performance of DTW-based systems in verification tasks, with the high potential for skilled forgery detection of the Kinematic Theory of rapid human movements. Experiments were carried out on the publicly available BiosecurID multimodal database, comprising 400 subjects. Results show that the performance of the DTW-based system improves for both skilled and random forgeries.This work has been partially supported by project Bio- Shield (TEC2012-34881) from Spanish MINECO, BEAT (FP7-SEC-284989) from EU, Cátedra UAM-Telefónica, CECABANK, and grant RGPIN-915 from NSERC Canada. M. G.-B. is supported by a FPU Fellowship from Spanish MECD

Feature Representation for Online Signature Verification

Biometrics systems have been used in a wide range of applications and have improved people authentication. Signature verification is one of the most common biometric methods with techniques that employ various specifications of a signature. Recently, deep learning has achieved great success in many fields, such as image, sounds and text processing. In this paper, deep learning method has been used for feature extraction and feature selection.Comment: 10 pages, 10 figures, Submitted to IEEE Transactions on Information Forensics and Securit

Introduction to Presentation Attacks in Signature Biometrics and Recent Advances

Applications based on biometric authentication have received a lot of interest in the last years due to the breathtaking results obtained using personal traits such as face or fingerprint. However, it is important not to forget that these biometric systems have to withstand different types of possible attacks. This chapter carries out an analysis of different Presentation Attack (PA) scenarios for on-line handwritten signature verification. The main contributions of this chapter are: i) an updated overview of representative methods for Presentation Attack Detection (PAD) in signature biometrics; ii) a description of the different levels of PAs existing in on-line signature verification regarding the amount of information available to the impostor, as well as the training, effort, and ability to perform the forgeries; and iii) an evaluation of the system performance in signature biometrics under different scenarios considering recent publicly available signature databases, DeepSignDB and SVC2021_EvalDB. This work is in line with recent efforts in the Common Criteria standardization community towards security evaluation of biometric systems.Comment: Chapter of the Handbook of Biometric Anti-Spoofing (Third Edition

Complexity-based Biometric Signature Verification

On-line signature verification systems are mainly based on two approaches: feature- or time functions-based systems (a.k.a. global and local systems). However, new sources of information can be also considered in order to complement these traditional approaches, reduce the intra-class variability and achieve more robust signature verification systems against forgers. In this paper we focus on the use of the concept of complexity in on-line signature verification systems. The main contributions of the present work are: 1) classification of users according to the complexity level of their signatures using features extracted from the Sigma LogNormal writing generation model, and 2) a new architecture for signature verification exploiting signature complexity that results in highly improved performance. Our proposed approach is tested considering the BiosecurID on-line signature database with a total of 400 users. Results of 5.8% FRR for a FAR = 5.0% have been achieved against skilled forgeries outperforming recent related works. In addition, an analysis of the optimal time functions for each complexity level is performed providing practical insights for the application of signature verification in real scenarios

Drawing, Handwriting Processing Analysis: New Advances and Challenges

International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline

Biometric Signature Verification Using Recurrent Neural Networks

“© 2017 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”Architectures based on Recurrent Neural Networks (RNNs) have been successfully applied to many different tasks such as speech or handwriting recognition with state-of-The art results. The main contribution of this work is to analyse the feasibility of RNNs for on-line signature verification in real practical scenarios. We have considered a system based on Long Short-Term Memory (LSTM) with a Siamese architecture whose goal is to learn a similarity metric from pairs of signatures. For the experimental work, the BiosecurID database comprised of 400 users and 4 separated acquisition sessions are considered. Our proposed LSTM RNN system has outperformed the results of recent published works on the BiosecurID benchmark in figures ranging from 17.76% to 28.00% relative verification performance improvement for skilled forgeriesThis work has been supported by project TEC2015-70627-R MINECO/FEDER and by UAM-CecaBank Project. Ruben Tolosana is supported by a FPU Fellowship from Spanish MEC

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

Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)

[Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”. Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità, e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”. A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine. The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art. The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics

Verificación de firma dinámica: Nuevos enfoques basados en la estabilidad de muestras y trazos

Trabajo Fin de Máster en Investigación e Innovación en la Inteligencia Computacional y Sistemas InteractivosEn este Trabajo de Fin de Máster se realiza un estudio de los sistemas de verificación basados en firma manuscrita dinámica desde un enfoque novedoso: el análisis de la estabilidad de los trazos de la firma genuina de un usuario, con el objetivo de mejorar el rendimiento de los sistemas de verificación actuales. En primer lugar, se hace un estudio de los sistemas de verificación de firma actuales que, en la gran mayoría de situaciones ya obtienen excelentes resultados. Sin embargo, los sistemas de verificación se enfrentan a dos dificultades: por un lado, las firmas introducidas mediante el dedo, más populares en estos días, no obtienen tan buenos resultados como las firmas introducidas mediante un útil específico de escritura como es un stylus. Por otro lado, los sistemas de verificación tienen que deben lidiar con falsificaciones cada vez de mayor calidad: deben ser capaces de identificar falsificaciones realizadas por impostores que tenían a su disposición toda la información relativa a la firma genuina a falsificar, lo que permite hacer a los impostores falsificaciones muy similares a las firmas genuinas de los usuarios. En segundo lugar, se ha realizado un estudio del impacto de la detección y utilización de los trazos y muestras más estables de una firma sobre el rendimiento de los sistemas actuales. Con los resultados obtenidos, se han estudiado y propuesto otras posibles métricas que puedan dar indicios de potenciales falsificaciones, como por ejemplo, el número de trazos en los que se divide una firma en base a su perfil de velocidad. Por último, una vez analizados los resultados obtenidos y extraídas las conclusiones al respecto, se ha propuesto una serie de líneas de investigación para trabajos futuro

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