Time-series averaging and local stability-weighted dynamic time warping for online signature verification

Okawa, Manabu

doi:10.1016/j.patcog.2020.107699

Cited by 53 publications

(31 citation statements)

References 40 publications

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“…Okawa proposed the use of Local Stability-Weighted Dynamic Time Warping (LS-DTW) method to verify online signatures. Experiments on MCYT-100 and SVC2004 online signature data sets had achieved good results, effectively improving the speed and accuracy of online signatures system [20].…”

Section: Classification In Signaturesmentioning

confidence: 97%

Handwritten Signature Verification Method Based on Improved Combined Features

et al. 2021

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As a behavior feature, handwritten signatures are widely used in financial and administrative institutions. The appearance of forged signatures will cause great property losses to customers. This paper proposes a handwritten signature verification method based on improved combined features. According to advanced smart pen technology, when writing a signature, offline images and online data of the signature can be obtained in real time. It is the first time to realize the combination of offline and online. We extract the static and dynamic features of the signature and verify them with support vector machine (SVM) and dynamic time warping (DTW) respectively. We use a small number of samples during the training stage, which solves the problem of insufficient number of samples to a certain extent. We get two decision scores while getting the verification result. Finally, we propose a score fusion method based on accuracy (SF-A), which combines offline and online features through score fusion and utilize the complementarity among classifiers effectively. Experimental results show that using different numbers of training samples to conduct experiments on local data sets, the false acceptance rate (FAR) and false reject rate (FRR) obtained are better than the offline or online verification results.

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Section: Classification In Signaturesmentioning

confidence: 97%

Handwritten Signature Verification Method Based on Improved Combined Features

et al. 2021

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“…In the study [15], they proposed a new single-template strategy that uses averaging templates and local stability weighted dynamic time warping (LS-DTW) to simultaneously improve the speed and accuracy of online signature verification in order to meet the latest demands for automated security systems. In this method, which is called Euclidean centroid-based DTW centroid average, it was adopted to obtain an effective average template set for each feature while maintaining intra-user variability among reference samples.…”

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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“…Based on [ 15 ], Forestier et al [ 16 ] proposed a weighted version of DBA (WDBA), and three weighting schemes: average all (AA), average selected (AS), and average selected with distance (ASD) given. In recent years, DAB and WDBA have been applied by several researchers in sequence classification and regression problems [ 8 , 17 , 18 , 19 ]. For example, Manabu Okawa [ 19 ] used the DBA to improve the rate of online signature verification.…”

Section: Introductionmentioning

confidence: 99%

A Novel Data Augmentation Method for Improving the Accuracy of Insulator Health Diagnosis

Song

et al. 2022

Sensors

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Performing ultrasonic nondestructive testing experiments on insulators and then using machine learning algorithms to classify and identify the signals is an important way to achieve an intelligent diagnosis of insulators. However, in most cases, we can obtain only a limited number of data from the experiments, which is insufficient to meet the requirements for training an effective classification and recognition model. In this paper, we start with an existing data augmentation method called DBA (for dynamic time warping barycenter averaging) and propose a new data enhancement method called AWDBA (adaptive weighting DBA). We first validated the proposed method by synthesizing new data from insulator sample datasets. The results show that the AWDBA proposed in this study has significant advantages relative to DBA in terms of data enhancement. Then, we used AWDBA and two other data augmentation methods to synthetically generate new data on the original dataset of insulators. Moreover, we compared the performance of different machine learning algorithms for insulator health diagnosis on the dataset with and without data augmentation. In the SVM algorithm especially, we propose a new parameter optimization method based on GA (genetic algorithm). The final results show that the use of the data augmentation method can significantly improve the accuracy of insulator defect identification.

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Time-series averaging and local stability-weighted dynamic time warping for online signature verification

Cited by 53 publications

References 40 publications

Handwritten Signature Verification Method Based on Improved Combined Features

Handwritten Signature Verification Method Based on Improved Combined Features

Signature Verification Using Siamese Neural Network One-Shot Learning

A Novel Data Augmentation Method for Improving the Accuracy of Insulator Health Diagnosis

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