Online Signature Verification Using Deep Learning and Feature Representation Using Legendre Polynomial Coefficients

Hefny, Amr; Moustafa, Mohamed

doi:10.1007/978-3-030-14118-9_68

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…Simpler approaches based on Multilayer Perceptron (MLP) were considered in [27]. Hefny and Moustafa considered Legendre polynomials coefficients as features to model the signatures.…”

Section: On-line Signature Verificationmentioning

confidence: 99%

DeepSign: Deep On-Line Signature Verification

Tolosana

Vera-Rodríguez

Fiérrez

et al. 2021

IEEE Trans. Biom. Behav. Identity Sci.

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Deep learning has become a breathtaking technology in the last years, overcoming traditional handcrafted approaches and even humans for many different tasks. However, in some tasks, such as the verification of handwritten signatures, the amount of publicly available data is scarce, what makes difficult to test the real limits of deep learning. In addition to the lack of public data, it is not easy to evaluate the improvements of novel proposed approaches as different databases and experimental protocols are usually considered.The main contributions of this study are: i) we provide an indepth analysis of state-of-the-art deep learning approaches for on-line signature verification, ii) we present and describe the new DeepSignDB on-line handwritten signature biometric public database 1 , iii) we propose a standard experimental protocol and benchmark to be used for the research community in order to perform a fair comparison of novel approaches with the state of the art, and iv) we adapt and evaluate our recent deep learning approach named Time-Aligned Recurrent Neural Networks (TA-RNNs) 2 for the task of on-line handwritten signature verification. This approach combines the potential of Dynamic Time Warping and Recurrent Neural Networks to train more robust systems against forgeries. Our proposed TA-RNN system outperforms the state of the art, achieving results even below 2.0% EER when considering skilled forgery impostors and just one training signature per user.

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“…Simpler approaches based on Multilayer Perceptron (MLP) were considered in [27]. Hefny and Moustafa considered Legendre polynomials coefficients as features to model the signatures.…”

Section: On-line Signature Verificationmentioning

confidence: 99%

DeepSign: Deep On-Line Signature Verification

Tolosana

Vera-Rodríguez

Fiérrez

et al. 2021

IEEE Trans. Biom. Behav. Identity Sci.

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“…In this context, several approaches have been proposed. [7] used Legendre Polynomial coefficients to get a fixed length of features which make signature matching easy. The verification step was based on shallow feedforward neural networks with K-fold cross validation and obtained the Equal Error Rate (EER) of 0.49 on SignComp2011 database.…”

Section: State Of the Artmentioning

confidence: 99%

Temporal Residual Networks basedbeta-elliptic model and multi-head attentionfor online handwriting signature verification

Zouari¹,

Abbasi²,

Boubaker³

et al. 2023

Preprint

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In this work, we proposed a new system for online handwriting sig-nature verification based on beta elliptic modeling and TemporalResidual Neural Networks based Multi-Head Attention model. Thebeta-elliptic modeling was applied to divide the handwriting signaturetrajectory into strokes by inspecting the extremum velocity instantsand extract their dynamic and geometric proprieties. In the verifica-tion process, residual networks based temporal convolution was devel-oped to deals with sequential input data. Furthermore, we appliedMulti head attention model to focus on a few particular aspectsat a time and carry out an efficient and sequential data process-ing . The experiments were done on two public databases SVC-2004and SCUT-MMSIG, and we achieved the state-of-the-art performanceswith an Equal Error Rates equals to 0.114 and 0. 133 respectively.

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“…In [14] they developed a deep learning-based online signature verification system. They used Legendre polynomial coefficients as a feature in order to model the signatures.…”

Section: Related Workmentioning

confidence: 99%

Signature Verification Using Siamese Neural Network One-Shot Learning

Arısoy

2021

International Journal of Engineering and Innovative Research

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With the acceleration of digitalization in all areas of our lives, the need for biometric verification methods is increasing. The fact that biometric data is unique and biometric verification is stronger against phishing attacks compared to password-based authentication methods, has increased its preference rate. Signature verification, which is one of the biometric verification types, plays an important role in many areas such as banking systems, administrative and judicial applications. There are 2 types of signature verification, online and offline, for identifying the identity of the person and detecting signature forgery. Online signature verification is carried out during signing and temporal dynamic data are available regarding the person's signature. Offline verification is applied by scanning the image after signing, and this verification is limited to spatial data. Therefore, the offline signature verification process is considered a more challenging task.In this study, offline signature verification, independent of the writer, based on One-Shot Learning, was performed using Siamese Neural Network. Due to the fact that the Deep Convolution Neural Network requires a large amount of labeled data for image classification, real and fake signature distinction has been achieved by using the One-Shot Learning method, which can perform a successful classification by using less numbers of signature images. As a result of the experiments conducted on signature datasets, using the Siamese architecture, the proposed approach achieved percentage accuracy of 93.23, 90.11, 89.99, 92.35 verification in 4NSigComp2012, SigComp2011, 4NSigComp2010 and BHsig260 respectively.

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Online Signature Verification Using Deep Learning and Feature Representation Using Legendre Polynomial Coefficients

Cited by 9 publications

References 17 publications

DeepSign: Deep On-Line Signature Verification

DeepSign: Deep On-Line Signature Verification

Temporal Residual Networks basedbeta-elliptic model and multi-head attentionfor online handwriting signature verification

Signature Verification Using Siamese Neural Network One-Shot Learning

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