Fusion of Local and Regional Approaches for On-Line Signature Verification

Fiérrez-Aguilar, J.; Krawczyk, Stephen; Ortega‐Garcia, Javier; Jain, Anil K.

doi:10.1007/11569947_24

Cited by 54 publications

(26 citation statements)

References 21 publications

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“…It was only outperformed by the winner of the competition, which was based on a Dynamic Time Warping approach (Kholmatov and Yanikoglu, 2005). Interestingly, it has been recently shown that the HMM approach outperforms an implementation of the DTW approach used by the winner when enough training signatures are available (Fierrez-Aguilar et al, 2005a). More comparative experiments with other state-of-the-art systems can be found in (Garcia-Salicetti et al, 2006).…”

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

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

Fiérrez-Aguilar

Ortega‐Garcia

Ramos

et al. 2007

Pattern Recognition Letters

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276

118

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Section: Comparison With the State-of-the-artmentioning

confidence: 99%

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

Fiérrez-Aguilar

Ortega‐Garcia

Ramos

et al. 2007

Pattern Recognition Letters

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276

118

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“…Even though these features are not so effective when they are used by themselves, they improved the performance when used together with other features. Moreover, our results outperformed the best result published on the SVC website 2 and reported in [24]. We only evaluated a few features in this study; combinations of many more features, such as those proposed in references [16][21]- [23], should improve the performance.…”

Section: Resultsmentioning

confidence: 54%

“…We considered ten combinations of five genuine signatures as reference signatures, and performed ten experiments while changing only the reference signatures. These were the same conditions as in the competition and the study described in reference [24].…”

Section: Svcmentioning

confidence: 99%

Effectiveness of Pen Pressure, Azimuth, and Altitude Features for Online Signature Verification

Muramatsu

Matsumoto

Lecture Notes in Computer Science

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Abstract. Many algorithms for online signature verification using multiple features have been proposed. Recently it has been argued that pen pressure, azimuth, and altitude can cause instability and deteriorate the performance. Algorithms without pen pressure and inclination features outperformed with them in SVC2004. However, we previously found that these features improved the performance in evaluations using our private database. The effectiveness of the features thus depended on the algorithm. Therefore, we re-evaluated our algorithm using the same database as used in SVC2004 and discuss the effectiveness of pen pressure, azimuth and altitude. Experimental results show that even though these features are not so effective when they are used by themselves, they improved the performance when used in combination with other features. When pen pressure and inclination features were considered, an EER of 3.61% was achieved, compared to an EER of 5.79% when these features were not used.

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“…Basically, the HMM represents a doubly stochastic process governed by an underlying Markov chain with finite number of states and a set of random functions each of which is associated with the output observation of one state [10]. There are many previous studies in which the system proposed for signature verification is based on HMM algorithms [11], [12]. In addition, a GMM-based system which can be seen as a particular case of HMM with only one hidden state is considered in this work.…”

Section: Introductionmentioning

confidence: 99%

Update strategies for HMM-based dynamic signature biometric systems

Tolosana

Vera-Rodríguez

Ortega‐Garcia

et al. 2015

2015 IEEE International Workshop on Information Forensics and Security (WIFS)

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract-Biometric authentication on devices such as smartphones and tablets has increased significantly in the last years. One of the most acceptable and increasing traits is the handwriting 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 systems 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.

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Fusion of Local and Regional Approaches for On-Line Signature Verification

Abstract: Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in:

Cited by 54 publications

References 21 publications

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

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

Effectiveness of Pen Pressure, Azimuth, and Altitude Features for Online Signature Verification

Update strategies for HMM-based dynamic signature biometric systems

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