Update Strategies for HMM-Based Dynamic Signature Biometric Systems

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. [R. Tolosana, R. Vera-Rodriguez, J. Ortega-Garcia and J. Fierrez, "Update strategies for HMM-based dynamic signature biometric systems," Information Forensics and Security (WIFS), 2015 IEEE International Workshop on, Rome, 2015, pp. 1-6. doi: 10.1109/WIFS.2015.7368583Biometric authentication on devices such as smart- phones and tablets has increased significantly in the last years. One of the most acceptable and increasing traits is the handwrit- ing signature as it has been used in financial and legal agreements scenarios for over a century. Nowadays, it is frequent to sign in banking and commercial areas on digitizing tablets. For these reasons, it is necessary to consider a new scenario where the number of training signatures available to generate the user template is variable and besides it has to be taken into account the lap of time between them (inter-session variability). In this work we focus on dynamic signature verification. The main goal of this work is to study system configuration update strategies of time functions-based systems such as Hidden Markov Model (HMM) and Gaussian Mixture Models (GMM). Therefore, two different cases have been considered. First, the usual case of having an HMM-based system with a fixed configuration (i.e. Baseline System). Second, an HMM-based and GMM-based sys- tems whose configurations are optimized regarding the number of training signatures available to generate the user template. The experimental work has been carried out using an extended version of the Signature Long-Term database taking into account skilled and random or zero-effort forgeries. This database is comprised of a total of 6 different sessions distributed in a 15-month time span. Analyzing the results, the Proposed Systems achieve an average absolute improvement of 4.6% in terms of EER(%) for skilled forgeries cases compared to the Baseline System whereas the average absolute improvement for the random forgeries cases is of 2.7% EER. These results show the importance of optimizing the configuration of the systems compared to a fixed configuration system when the number of training signatures available to generate the user template increases.This work was supported in part by the Project Bio-Shield (TEC2012-34881), in part by Cecabank e-BioFirma Contract, in part by the BEAT Project (FP7-SEC-284989) and in part by Catedra UAM-Telefonica

Offline Handwritten Signature Verification - Literature Review

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory, Tools and Applications (IPTA 2017

MobiBits: Multimodal Mobile Biometric Database

This paper presents a novel database comprising representations of five different biometric characteristics, collected in a mobile, unconstrained or semi-constrained setting with three different mobile devices, including characteristics previously unavailable in existing datasets, namely hand images, thermal hand images, and thermal face images, all acquired with a mobile, off-the-shelf device. In addition to this collection of data we perform an extensive set of experiments providing insight on benchmark recognition performance that can be achieved with these data, carried out with existing commercial and academic biometric solutions. This is the first known to us mobile biometric database introducing samples of biometric traits such as thermal hand images and thermal face images. We hope that this contribution will make a valuable addition to the already existing databases and enable new experiments and studies in the field of mobile authentication. The MobiBits database is made publicly available to the research community at no cost for non-commercial purposes.Comment: Submitted for the BIOSIG2018 conference on June 18, 2018. Accepted for publication on July 20, 201

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

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

Verificación de firmas en línea usando modelos de mezcla Gaussianas y estrategias de aprendizaje para conjuntos pequeños de muestras

RESUMEN: El artículo aborda el problema de entrenamiento de sistemas de verificación de firmas en línea cuando el número de muestras disponibles para el entrenamiento es bajo, debido a que en la mayoría de situaciones reales el número de firmas disponibles por usuario es muy limitado. El artículo evalúa nueve diferentes estrategias de clasificación basadas en modelos de mezclas de Gaussianas (GMM por sus siglas en inglés) y la estrategia conocida como modelo histórico universal (UBM por sus siglas en inglés), la cual está diseñada con el objetivo de trabajar bajo condiciones de menor número de muestras. Las estrategias de aprendizaje de los GMM incluyen el algoritmo convencional de Esperanza y Maximización, y una aproximación Bayesiana basada en aprendizaje variacional. Las firmas son caracterizadas principalmente en términos de velocidades y aceleraciones de los patrones de escritura a mano de los usuarios. Los resultados muestran que cuando se evalúa el sistema en una configuración genuino vs. impostor, el método GMM-UBM es capaz de mantener una precisión por encima del 93%, incluso en casos en los que únicamente se usa para entrenamiento el 20% de las muestras disponibles (equivalente a 5 firmas), mientras que la combinación de un modelo Bayesiano UBM con una Máquina de Soporte Vectorial (SVM por sus siglas en inglés), modelo conocido como GMM-Supervector, logra un 99% de acierto cuando las muestras de entrenamiento exceden las 20. Por otro lado, cuando se simula un ambiente real en el que no están disponibles muestras impostoras y se usa únicamente el 20% de las muestras para el entrenamiento, una vez más la combinación del modelo UBM Bayesiano y una SVM alcanza más del 77% de acierto, manteniendo una tasa de falsa aceptación inferior al 3%.ABSTRACT: This paper addresses the problem of training on-line signature verification systems when the number of training samples is small, facing the real-world scenario when the number of available signatures per user is limited. The paper evaluates nine different classification strategies based on Gaussian Mixture Models (GMM), and the Universal Background Model (UBM) strategy, which are designed to work under small-sample size conditions. The GMM’s learning strategies include the conventional Expectation-Maximisation algorithm and also a Bayesian approach based on variational learning. The signatures are characterised mainly in terms of velocities and accelerations of the users’ handwriting patterns. The results show that for a genuine vs. impostor test, the GMM-UBM method is able to keep the accuracy above 93%, even when only 20% of samples are used for training (5 signatures). Moreover, the combination of a full Bayesian UBM and a Support Vector Machine (SVM) (known as GMM-Supervector) is able to achieve 99% of accuracy when the training samples exceed 20. On the other hand, when simulating a real environment where there are not available impostor signatures, once again the combination of a full Bayesian UBM and a SVM, achieve more than 77% of accuracy and a false acceptance rate lower than 3%, using only 20% of the samples for training

Multibiometric security in wireless communication systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims

Online signature verification techniques

Signature is a behavioral biometric: it is not based on the physical properties, such as fingerprint or face, of the individual, but behavioral ones. Signature verification is split into two according to the available data in the input. Offline (static) signature verification takes as input the image of a signature and is useful in automatic verification of signatures found on bank checks and documents. Online (dynamic) signature verification uses signatures that are captured by pressure-sensitive tablets that extract dynamic properties of a signature in addition to its shape. The purpose of project is to develop an authentication system based on personal signatures. Signature verification is an important research topic in the area of biometric authentication. In this project the work is done in such a way that the signatures are captured using WEBCAM. A visual-based online signature verification system in which the signer’s pen tip is tracked. The data acquisition of the system consists of only low-cost cameras (webcams) and does not need special equipment such as an electronic tablet. Online signature data is obtained from the images captured by the webcams by tracking the pen tip. The pen tip tracking is implemented by the Sequential Monte Carlo method in real time. Then, the distance between the input signature data and reference signature data enrolled in advance is computed using Dynamic Time Warping (DTW). Finally, the input signature is classified as genuine or a forgery by comparing the distance with a threshold

Evaluation and analysis of hybrid intelligent pattern recognition techniques for speaker identification

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy