Online Signature Verification Based on DCT and Sparse Representation

Liu, Yishu; Yang, Zhihua; Yang, Lihua

doi:10.1109/tcyb.2014.2375959

Cited by 101 publications

(41 citation statements)

References 40 publications

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“…EER of our model is lower than the EER of the state of the art models except the model proposed by Liu et al [25] . The results obtained demonstrated that the proposed model performs better than most of the functional as well as parametric models as given in Table 9.…”

Section: Comparative Studycontrasting

confidence: 63%

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Online signature verification based on writer dependent features and classifiers

Manjunatha

Manjunath

Guru

et al. 2016

Pattern Recognition Letters

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a b s t r a c tIn this work, an approach for online signature verification based on writer specific features and classifier is investigated. Existing models for online signatures are generally writer independent, as a common classifier or fusion of classifier is used on a common set of features for all writers during verification. In contrast, our approach is based on the usage writer dependent features as well as writer dependent classifier. The two decisions namely optimal features suitable for a writer and a classifier to be used for authenticating the writer are taken based on the error rate achieved with the training samples. The performance of our model is tested on both MCYT-100 (DB1), a sub corpus of MCYT data set, consisting of signatures of 100 writers, MCYT-330 (DB2) consisting of signatures of all 330 writers and visual subcorpus of SUSIG dataset. Experimental results confirm the effectiveness of writer dependent characteristics for online signature verification. The error rate that we achieved is lower when compared to many existing contemporary works on online signature verification especially when the number of training samples available for each writer is sufficient enough.

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Section: Comparative Studycontrasting

confidence: 63%

“…A parametric based approach results in more compact representation as the entire signature is represented by means of a few parameters [25,36,38] .…”

Section: Introductionmentioning

confidence: 99%

Online signature verification based on writer dependent features and classifiers

Manjunatha

Manjunath

Guru

et al. 2016

Pattern Recognition Letters

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“…The most common algorithms employed in time functionsbased systems are DTW (Dynamic Time Warping) [8], HMM (Hidden Markov Models) [9] [5], NN (Neural Networks) [10] and SVM (Support Vector Machines) [11]. DTW has the advantage that it does not need a previous training of the user models.…”

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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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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“…Signature verification techniques can be classified into two approaches: function-based and feature-based [20]. Function-based signature verification refers to the matching process using the original time-series data of a signature.…”

Section: Related Workmentioning

confidence: 99%

Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

et al. 2018

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This paper proposes a dynamic verification scheme for finger-drawn signatures in smartphones. As a dynamic feature, the movement of a smartphone is recorded with accelerometer sensors in the smartphone, in addition to the moving coordinates of the signature. To extract high-level longitudinal and topological features, the proposed scheme uses a convolution neural network (CNN) for feature extraction, and not as a conventional classifier. We assume that a CNN trained with forged signatures can extract effective features (called S-vector), which are common in forging activities such as hesitation and delay before drawing the complicated part. The proposed scheme also exploits an autoencoder (AE) as a classifier, and the S-vector is used as the input vector to the AE. An AE has high accuracy for the one-class distinction problem such as signature verification, and is also greatly dependent on the accuracy of input data. S-vector is valuable as the input of AE, and, consequently, could lead to improved verification accuracy especially for distinguishing forged signatures. Compared to the previous work, i.e., the MLP-based finger-drawn signature verification scheme, the proposed scheme decreases the equal error rate by 13.7%, specifically, from 18.1% to 4.4%, for discriminating forged signatures.

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Online Signature Verification Based on DCT and Sparse Representation

Cited by 101 publications

References 40 publications

Online signature verification based on writer dependent features and classifiers

Online signature verification based on writer dependent features and classifiers

Increasing the robustness of biometric templates for dynamic signature biometric systems

Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

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