A new algorithm for signature verification system based on DTW and GMM

Miguel-Hurtado, Oscar; Mengibar-Pozo, L.; Pacut, Andrzej

doi:10.1109/ccst.2008.4751303

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…Better performance than those which are shown in this paper has been obtained using both algorithms together [8]. However, for the purpose of this study, in order to analyze the impact of the compact data formats in two specific kinds of algorithms, DTW and GMM will be used separately.…”

Section: Performance Of the Three Signatures Formatsmentioning

confidence: 83%

Analysis on compact data formats for the performance of handwritten signature biometrics

Miguel-Hurtado

Mengibar-Pozo

Tomeo-Reyes

et al. 2009

43rd Annual 2009 International Carnahan Conference on Security Technology

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This paper deals with the Signature Data Formats proposed by ISO 19794 project: 19794 part 7 Full Format and Compact Format (published in 2007) and the new 19794 part 11, which is under development. It will be shown how these formats handle the raw data coming from a Signature Input Device, and what the size of a Biometric Information Record is for each one.Another compression method, using LZ77 compression algorithm, is proposed and tested. The paper will also show the impact of using these compact formats on the performance of two different algorithms: Dynamic Time Warping and Gaussian Mixture Models. MCyT and SVC2004 signature databases have been used to carry out all tests.

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Section: Performance Of the Three Signatures Formatsmentioning

confidence: 83%

Analysis on compact data formats for the performance of handwritten signature biometrics

Miguel-Hurtado

Mengibar-Pozo

Tomeo-Reyes

et al. 2009

43rd Annual 2009 International Carnahan Conference on Security Technology

Self Cite

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“…Miguel-Hurtado et al, in [12], continued their previous work of combining Dynamic Time Warping (DTW) and Gaussians Mixture Modeling (GMM) algorithms. The Dynamic Time Warping algorithm is used for nonlinear time X-axis alignment and to impact minimization from other handwriting.…”

Section: Literature Reviewmentioning

confidence: 90%

Forensic Analysis of Offline Signatures Using Multilayer Perceptron and Random Forest

Shah¹,

Shah²,

Fayaz³

et al. 2017

IJDTA

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“…The database contains 25 genuine signatures and 25 skilled forgeries for 100 different users. Basically, the implemented algorithm is an improved version of the presented one [19] by adding dynamic features, such as speeds and accelerations, at the GMM matching.…”

Section: Signature Verification Algorithmmentioning

confidence: 99%

“…The algorithm performance was tested by the public database available in [18]. The DET (Detection Error Tradeoff) curve, shown in Figure 2, represents the relationship between FMR (False Match Rate) and FNMR (False Non-Match Rate) for different threshold decision values [16,19]. The EER (Equal Error Rate) achieved by the proposed online signature algorithm is 2.74%, which is a competitive performance when compared to other biometric modalities presented in some competitions [20].…”

Section: Signature Verification Algorithmmentioning

confidence: 99%

Online Signature Verification Systems on a Low-Cost FPGA

2021

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This paper describes three different approaches for the implementation of an online signature verification system on a low-cost FPGA. The system is based on an algorithm, which operates on real numbers using the double-precision floating-point IEEE 754 format. The double-precision computations are replaced by simpler formats, without affecting the biometrics performance, in order to permit efficient implementations on low-cost FPGA families. The first approach is an embedded system based on MicroBlaze, a 32-bit soft-core microprocessor designed for Xilinx FPGAs, which can be configured by including a single-precision floating-point unit (FPU). The second implementation attaches a hardware accelerator to the embedded system to reduce the execution time on floating-point vectors. The last approach is a custom computing system, which is built from a large set of arithmetic circuits that replace the floating-point data with a more efficient representation based on fixed-point format. The latter system provides a very high runtime acceleration factor at the expense of using a large number of FPGA resources, a complex development cycle and no flexibility since it cannot be adapted to other biometric algorithms. By contrast, the first system provides just the opposite features, while the second approach is a mixed solution between both of them. The experimental results show that both the hardware accelerator and the custom computing system reduce the execution time by a factor ×7.6 and ×201 but increase the logic FPGA resources by a factor ×2.3 and ×5.2, respectively, in comparison with the MicroBlaze embedded system.

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A new algorithm for signature verification system based on DTW and GMM

Cited by 6 publications

References 10 publications

Analysis on compact data formats for the performance of handwritten signature biometrics

Analysis on compact data formats for the performance of handwritten signature biometrics

Forensic Analysis of Offline Signatures Using Multilayer Perceptron and Random Forest

Online Signature Verification Systems on a Low-Cost FPGA

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