HMM-based on-line signature verification: Feature extraction and signature modeling

Fiérrez-Aguilar, J.; Ortega‐Garcia, Javier; Ramos, Daniel; González-Rodríguez, Joaquín

doi:10.1016/j.patrec.2007.07.012

Cited by 280 publications

(125 citation statements)

References 35 publications

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“…A time functions-based system based on previous works [5], [19] is considered. Only time functions related to X, Y coordinates and pressure are considered in this work.…”

Section: B Feature Extraction and Selectionmentioning

confidence: 99%

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Increasing the robustness of biometric templates for dynamic signature biometric systems

Tolosana

Vera-Rodríguez

Ortega‐Garcia

et al. 2015

2015 International Carnahan Conference on Security Technology (ICCST)

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El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription Abstract-Due to the high deployment of devices such as smartphones and tablets and their increasing popularity in our society, the use of biometric traits in commercial and banking applications through these novel devices as an easy, quick and reliable way to perform payments is rapidly increasing. The handwritten signature is one of the most socially accepted biometric traits in these sectors due to the fact that it has been used in financial and legal transitions for centuries. In this paper we focus on dynamic signature verification systems. Nowadays, most of the state-of-the-art systems are based on extracting information contained in the X and Y spatial position coordinates of the signing process, which is stored in the biometric templates. However, it is critical to protect this sensible information of the users signatures against possible external attacks that would allow criminals to perform direct attacks to a biometric system or carry out high quality forgeries of the users signatures. Following this problem, the goal of this work is to study the performance of the system in two cases: first, an optimal time functions-based system taking into account the information related to X and Y coordinates and pressure, which is the common practice (i.e. Standard System). Second, we study an extreme case not considering information related to X, Y coordinates and their derivatives on the biometric system (i.e. Secure System), which would be a much more robust system against attacks, as this critical information would not be stored anywhere. The experimental work is carried out using e-BioSign database which makes use of 5 devices in total. The systems considered in this work are based on Dynamic Time Warping (DTW), an elastic measure over the selected time functions. Sequential Forward Features Selection (SFFS) is applied as a reliable way to obtain an optimal time functions vector over a development subset of users of the database. The results obtained over the evaluation subset of users of the database show a similar performance for both Standard and Secure Systems. Therefore, the use of a Secure System can be useful in some applications such as banking in order to avoid the lost of important user information against possible external attacks.

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“…A time functions-based system based on previous works [5], [19] is considered. Only time functions related to X, Y coordinates and pressure are considered in this work.…”

Section: B Feature Extraction and Selectionmentioning

confidence: 99%

“…X and Y pen coordinates, pressure, etc.) for verification [5]. Traditionally, time functions-based systems have achieved better recognition performance than featurebased systems [6], [7], [3].…”

Section: Introductionmentioning

confidence: 99%

Increasing the robustness of biometric templates for dynamic signature biometric systems

Tolosana

Vera-Rodríguez

Ortega‐Garcia

et al. 2015

2015 International Carnahan Conference on Security Technology (ICCST)

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“…However, there are very few research works in the field of dynamic signature for forensic examinations [1,16,8]. The majority of relevant literature regarding dynamic signature analysis is in the field of biometric recognition [4], which make use of algorithms such as Hidden Markov Models [5,18] or Dynamic Time Warping [13,17].…”

Section: Introductionmentioning

confidence: 99%

e-BioSign tool: Towards scientific assessment of dynamic signatures under forensic conditions

Vera-Rodríguez

Fiérrez

Ortega‐Garcia

et al. 2015

2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS)

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“…For online signature verification, many classifiers have been attempted by different researchers such as distance based classifier [5], HMM [6,7], SVM [8], PNN [9], Bayesian [10], Symbolic classifier [11], Random Forest [8]. The performance of a verification system is measured in terms of two error rates namely false acceptance rate (FAR) and false rejection rate (FAR).…”

Section: Introductionmentioning

confidence: 99%

Cluster Dependent Classifiers for Online Signature Verification

Manjunath

Manjunatha

Guru

et al. 2015

Mining Intelligence and Knowledge Exploration

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Abstract. In this paper, the applicability of notion of cluster dependent classifier for online signature verification is investigated. For every writer, by the use of a number of training samples, a representative is selected based on minimum average distance criteria (centroid) across all the samples of that writer. Later k-means clustering algorithm is employed to cluster the writers based on the chosen representatives. To select a suitable classifier for a writer, the equal error rate (EER) is estimated using each of the classifier for every writer in a cluster. The classifier which gives the lowest EER for a writer is selected to be the suitable classifier for that writer. Once the classifier for each writer in a cluster is decided, the classifier which has been selected for a maximum number of writers in that cluster is decided to be the classifier for all writers of that cluster. During verification, the authenticity of the query signature is decided using the same classifier which has been selected for the cluster to which the claimed writer belongs. In comparison with the existing works on online signature verification, which use a common classifier for all writers during verification, our work is based on the usage of a classifier which is cluster dependent. On the other hand our intuition is to recommend to use a same classifier for all and only those writers who have some common characteristics and to use different classifiers for writers of different characteristics. To demonstrate the efficacy of our model, extensive experiments are carried out on the MCYT online signature dataset (DB1) consisting signatures of 100 individuals. The outcome of the experiments being indicative of increased performance with the adaption of cluster dependent classifier seems to open up a new avenue for further investigation on a reasonably large dataset.

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HMM-based on-line signature verification: Feature extraction and signature modeling

Abstract: Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in:

Cited by 280 publications

References 35 publications

Increasing the robustness of biometric templates for dynamic signature biometric systems

Increasing the robustness of biometric templates for dynamic signature biometric systems

e-BioSign tool: Towards scientific assessment of dynamic signatures under forensic conditions

Cluster Dependent Classifiers for Online Signature Verification

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