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This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters 28.16 (2007): 2325 – 2334, DOI: 10.1016/j.patrec.2007.07.012, A function-based approach to on-line signature verification is presented. The system uses a set of time sequences and Hidden Markov Models (HMMs). Development and evaluation experiments are reported on a subcorpus of the MCYT bimodal biometric database comprising more than 7,000 signatures from 145 subjects. The system is compared to other state-of-the-art systems based on the results of the First International Signature Verification Competition (SVC 2004). A number of practical findings related to feature extraction and modeling are obtained., This work has been supported by the Spanish projects TIC2003-08382-C05- 01 and TEC2006-13141-C03-03, and by the European NoE Biosecure.

This thesis considers the possibility of discretizing visually acquired signatures to represent them as an unordered collection of words and then use Sequential Minimal Optimization (SMO) for training Support Vector Machine (SVM) classifiers to classify the signatures as either legitimate or forged. Signature discretization is done using Symbolic Aggregate Approximation (SAX). SAX reduces the dimensions of the signature and produces a list of SAX words that are then represented as a Bag-of-Patterns (BoP) model for classification purposes. Each SAX word will act as a separate feature and can be evaluated by using techniques like Chi-squared for informativeness. This will lead us to the set of SAX words that were used by the classifier to make the decision. These SAX words can then be mapped to their corresponding original signature coordinates. The approach was tested on a dataset provided by California Institute of Technology that contains 3960 signatures of 106 subjects distributed across two sets. This dataset includes a set of original signature samples as well as forged signature samples for each subject. The classification accuracy achieved is promising and the extracted features provide insight into the unique patterns present in each of the subject's signature.

© 2014 by the author(s). The article is devoted to the study of a possibility of on-line signature verification using the wavelet transform with the radial basis function. Representation of the signature in the form of function, invariant concerning a position, - the signature is replaced by a broken curve and is described using the angles between the adjacent links - is offered. Using the wavelet transforms for the signature description, comparison of expansion coefficients of functions of signatures by the radial basis function to verify signatures is described in detail. In the result of application of the offered method the magnitude of error of first kind has made 4.4%, the magnitude of error of second kind - 2.8%.

International audience; In this paper, we propose to use the minimum jerk principle for representing on-line signatures. We briefly describe the minimum jerk model and the automatic procedure we propose for its implementation with signatures. Results on the MCYT-100 signature database are analysed regarding reconstruction error, residual analysis and stability. These results show that, despite its simplicity, the proposed model agrees with previous works on signature modelling and entropy-based signature categorization. Finally, possible applications and future works are suggested.

Paper present statistical based approach to signature verification with user oriented signature feature weights.

Personal signature has become an important part of human identity verification. Therefore, it is important to protect ones signature and be able to detect signature forgery. We present a statistical based approach to signature verification with user oriented signature feature weights. Only basic on-line signature features are used in the verification process. Depending on the authentication threshold, our method achieves satisfying FAR for unskilled and skilled forgery, but there is still some research to be done to improve user experience (reduce FRR).

In this work an on-line signature authentication system based on an ensemble of local, regional, and global matchers is presented. Specifically, the following matching approaches are taken into account: the fusion of two local methods employing Dynamic Time Warping, a Hidden Markov Model based approach where each signature is described by means of its regional properties, and a Linear Programming Descriptor classifier trained by global features. Moreover, a template protection scheme employing the BioHashing and the BioConvolving approaches, two well known template protection techniques for biometric recognition, is discussed. The reported experimental results, evaluated on the public MCYT signature database, show that our best ensemble obtains an impressive Equal Error Rate of 3%, when only five genuine signatures are acquired for each user during enrollment. Moreover, when the proposed protected system is taken into account, the Equal Error Rate achieved in the worst case scenario, that is,when an "impostor" is able to steal the hash keys, is equal to 4.51%, whereas an Equal Error Rate ∼0 can be obtained when nobody steals the hash keys. © 2009 Elsevier Ltd. All rights reserved.

In this paper, we propose a protected on-line signature based biometric authentication system. The original signature templates are protected by transforming them in a non-invertible way. Recovering the original biometrics from the stored data is thus computationally as hard as random guessing. The transformed templates are compared employing a Dynamic Time Warping (DTW) matching strategy. The reported experimental results, evaluated on the public MCYT signature database, show that the recognition rates are only slightly affected by the proposed protection scheme, which is able to guarantee the desired security for the employed biometrics. © 2009 IEEE.

In this chapter the authentication performances achievable by on-line signature recognition systems employing feature based and function based classifiers are analyzed. Specifically, three different stand-alone matchers, namely Hidden Markov Models, Parzen Windows classifier, and Symbolic classifier, are compared. An ensemble of the considered matchers, which significantly improves the system verification performances, is also proposed. Moreover, the usefulness of a feature selection process, and of a random feature subspaces approach for on-line signature verification, are also evaluated. The present work also investigates the authentication performance achievable when protected on-line signature based recognition systems are considered, by exploiting the BioConvolving template protection scheme. An extensive set of experimental results, obtained by using the public MCYT on-line signature subcorpus, which comprises 100 users, for each of which 25 genuine signatures and 25 skilled forgeries have been captured, are also presented.

Producción Científica, A new DTW-based on-line signature verification system is presented and evaluated. The system is specially designed to operate under realistic conditions, it needs only a small number of genuine signatures to operate and it can be deployed in almost any signature capable capture device. Optimal features sets have been obtained experimentally, in order to adapt the system to environments with different levels of security. The system has been evaluated using four on-line signature databases (MCYT, SVC2004, BIOMET and MyIDEA) and its performance is among the best systems reported in the state of the art. Average EERs over these databases lay between 0.41% and 2.16% for random and skilled forgeries respectively., Junta de Castilla y León (project VA077A08)

The representation of a biometric trait through a set of parametric features is commonly employed in many biometric authentication systems. In order to avoid any loss of useful information, large sets of features have been defined for biometric characteristics such as signature, gait or face. However, the proposed sets often contain features which are irrelevant, correlated with other features, or even unreliable. In this paper we propose two different approaches for the selection of those features which guarantee the best recognition performances. Moreover, we also face the problem of the binary representation of the selected features. Specifically, an algorithm which selects the minimum number of bits which should be assigned to a given feature, in order to not affect the recognition performances, is here proposed. The effectiveness of the proposed approaches is tested considering a watermarking based on-line signature authentication system, and employing the public MCYT online signature corpus as experimental database. © Springer-Verlag Berlin Heidelberg 2009.

A novel method for building an ensemble of on-line signature verification systems based on one-class classifiers is presented. The ensemble is built concatenating the classifiers obtained by the Random Subspace on the ''original features'' and a set of classifiers each trained selecting a different set of ''artificial features'' for each different subset of users that belong to the validation set. The ''artificial features'' are extracted using an OverComplete global feature combination, starting from a set of global features a set of artificial features is created by applying mathematical operators to a randomly extracted set of the original ones, then a small subset is selected for verification by running sequential forward floating selection (SFFS). Finally a set of One-class classifiers are used to classify, between genuine and impostor, each match between two signatures. As dataset the MCYT signature database is used, our results show that the proposed ensemble outperforms the ensembles based only on the original features. Using only 5 genuine signatures for each user our best system obtains an equal error rate of 4.5 in the skilled forgeries and 1.4 in the Random Forgeries, when 20 genuine signatures are used to train the classifiers an equal error rate of 2.2 in the skilled forgeries and 0.5 in the Random Forgeries are obtained.

International audience; In this article, we propose an original way to characterize information content in Online Signatures through a client-entropy measure based on local density estimation by a Hidden Markov Model. We show that this measure can be used to categorize signatures in visually coherent classes that can be related to complexity and variability criteria. Besides, the generated categories are coherent across four different databases: BIOMET, MCYT-100, BioSecure data subsets DS2 and DS3. This measure allows a comparison of databases in terms of clients’ signatures according to their information content

In this paper a novel method for obtaining an appropriate representation of patterns is presented. The information is extracted using an over-complete global feature combination, and then the most useful features are selected by sequential forward floating selection (SFFS). This new method has been tested in two problems: trained integration of iris and face biometrics; on-line signature verification system based on global information and a one-class classifier (Parzen Window Classifier). To the best of our knowledge, this is the first work that studies and proposes a set of ''artificial'' features for combining biometric matchers, created starting from the scores of the matchers. We show that a classifier trained on such set of features gains a noticeable performance improvement with respect to fixed fusion rules and other trained fusion methods. Moreover, we show that an on-line signature matcher based on the ''artificial'' features gains a noticeable performance improvement with respect to a matcher based on the ''original'' global features.

In this work, an on-line signature verification system based on local information and on a one-class classifier, the Linear Programming Descriptor classifier (LPD), is presented. The information is extracted as time functions of various dynamic properties of the signatures, then the discrete 1-D wavelet transform (WT) is performed on these features. The Discrete Cosine Transform (DCT) is used to reduce the approximation coefficients vector obtained by WT to a feature vector of a given dimension. The Linear Programming Descriptor classifier is trained using the DCT coefficients. Finally, we have studied the fusion among the approach here proposed and the state-of-the-art of the regional, the local and the global approaches. The fusion outperforms all the stand-alone approaches. Results using all the 5000 signatures from the 100 subjects of the SUBCORPUS-100 MCYT Bimodal Biometric Database are presented, yielding remarkable performance improvement both with random and skilled forgeries. We want to stress that our best fusion approach obtains an Equal Error Rate of 5.2% in the Skilled Forgeries, this value is the lowest Equal Error Rate reported in the literature for the SUBCORPUS-100 MCYT.

Javier Galbally ; Julian Fierrez ; Javier Ortega-Garcia; "Classification of handwritten signatures based on name legibility", Biometric Technology for Human Identification IV, Proc. SPIE 6539 (April 12, 2007); doi:10.1117/12.719236. Copyright 2007 Society of Photo‑Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited., Proceedings of the IV Biometric Technology for Human Identification (Orlando, Florida, USA), An automatic classification scheme of on-line handwritten signatures is presented. A Multilayer Perceptron (MLP) with a hidden layer is used as classifier, and two different signature classes are considered, namely: legible and non-legible name. Signatures are represented considering different feature subsets obtained from global information. Mahalanobis distance is used to rank the parameters and feature selection is then applied based on the top ranked features. Experimental results are given on the MCYT signature database comprising 330 signers. It is shown experimentally that automatic on-line signature classification based on the name legibility is feasible., This work was supported by the Spanish Ministry of Science and Technology under project TIC2003-08382-C05-01 and the European Network of Excellence Biosecure (IST-2002-507634).

M. Freire-Santos ; J. Fierrez-Aguilar ; J. Ortega-Garcia; "Cryptographic key generation using handwritten signature", Biometric Technology for Human Identification III, Proc. SPIE 6202 (April 17, 2006); doi:10.1117/12.665875. Copyright 2006 Society of Photo‑Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited., Proceedings of the III Biometric Technology for Human Identification (Orlando, Florida, USA), Based on recent works showing the feasibility of key generation using biometrics, we study the application of handwritten signature to cryptography. Our signature-based key generation scheme implements the cryptographic construction named fuzzy vault. The use of distinctive signature features suited for the fuzzy vault is discussed and evaluated. Experimental results are reported, including error rates to unlock the secret data by using both random and skilled forgeries from the MCYT database., This work has been supported by Spanish MCYT TIC2003-08382-C05-01 and by European Commission IST-2002-507634 Biosecure NoE projects.

This paper introduces and motivates the use of Gaussian Mixture Models (GMMs) for on-line signature verification. The individual Gaussian components are shown to represent some local, signer-dependent features that characterise spatial and temporal aspects of a signature, and are effective for modelling its specificity. The focus of this work is on automated order selection for signature models, based on the Minimum Description Length (MDL) principle. A complete experimental evaluation of the Gaussian Mixture signature models is conducted on a 50-user subset of the MCYT multimodal database. Algorithmic issues are explored and comparisons to other commonly used on-line signature modelling techniques based on Hidden Markov Models (HMMs) are made.