Quantifying dynamic time warping distance using probabilistic model in verification of dynamic signatures

Al‐Hmouz, Rami; Pedrycz, Witold; Daqrouq, Khaled; Morfeq, Ali; Al-Hmouz, Ahmed

doi:10.1007/s00500-017-2782-5

Cited by 24 publications

(12 citation statements)

References 19 publications

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“…On the x-axis is the false positive rate (FPR) and on the y-axis the true positive rate (TPR). The FPR and TPR are given in percent and are calculated as shown below (see Equations (31) and (32)):…”

Section: Experimental Methodologymentioning

confidence: 99%

“…It is known that signatures written by the same person differ slightly from time to time. Therefore, in References [7,28,32,33], only the part of less fluctuation is considered as the stable feature of handwriting in the signing process. The forward variance along the forward direction from the starting point and the backward variance from the end point along the backward direction are calculated.…”

Section: Of 25mentioning

confidence: 99%

“…Considerable attention on time features was attracted by References [32][33][34][35][36]. The distribution on the handwriting feature pairs age-time up and age-time down were analyzed in Reference [35] with the goal to obtain better knowledge about the correlation between handwriting and the age.…”

Section: Of 25mentioning

confidence: 99%

“…The use of Dynamic Time Warping (DTW) on signature verification can be found in the state-of-the-art methods too with good results [32,50,51]. Its use on handwritten text is new, and it is an innovation of this proposal.…”

Section: Of 25mentioning

confidence: 99%

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Writer Identification Using Handwritten Cursive Texts and Single Character Words

et al. 2019

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One of the biometric methods in authentication systems is the writer verification/identification using password handwriting. The main objective of this paper is to present a robust writer verification system by using cursive texts as well as block letter words. To evaluate the system, two datasets have been used. One of them is called Secure Password DB 150, which is composed of 150 users with 18 samples of single character words per user. Another dataset is public and called IAM online handwriting database, and it is composed of 220 users of cursive text samples. Each sample has been defined by a set of features, composed of 67 geometrical, statistical, and temporal features. In order to get more discriminative information, two feature reduction methods have been applied, Fisher Score and Info Gain Attribute Evaluation. Finally, the classification system has been implemented by hold-out cross validation and k-folds cross validation strategies for three different classifiers, K-NN, Naïve Bayes and Bayes Net classifiers. Besides, it has been applied for verification and identification approaches. The best results of 95.38% correct classification are achieved by using the k-nearest neighbor classifier for single character DB. A feature reduction by Info Gain Attribute Evaluation improves the results for Naïve Bayes Classifier to 98.34% for IAM online handwriting DB. It is concluded that the set of features and its reduction are a strong selection for the based-password handwritten writer identification in comparison with the state-of-the-art.

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Section: Experimental Methodologymentioning

confidence: 99%

Section: Of 25mentioning

confidence: 99%

Section: Of 25mentioning

confidence: 99%

Section: Of 25mentioning

confidence: 99%

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Writer Identification Using Handwritten Cursive Texts and Single Character Words

et al. 2019

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“…Based on the predefined threshold, the test signature is delegated as genuine or counterfeit. The other function‐based OSV systems incorporate interval‐valued classifier [7, 8], hidden Markov model (HMM) [4], dynamic time warping [1, 9–12], edit distance [13], neuro fuzzy [14], Gaussian mixture models [1], support vector machine [15] and neural machine [16], long short term memory (LSTM) [12, 13, 17–20] etc.…”

Section: Introductionmentioning

confidence: 99%

DeepFuseOSV: online signature verification using hybrid feature fusion and depthwise separable convolution neural network architecture

et al. 2020

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Impact of Type of Convolution Operation on Performance of Convolutional Neural Networks for Online Signature Verification

Vorugunti

Subramanian²,

Gautam³

et al. 2022

Lecture Notes in Computer Science

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Quantifying dynamic time warping distance using probabilistic model in verification of dynamic signatures

Cited by 24 publications

References 19 publications

Writer Identification Using Handwritten Cursive Texts and Single Character Words

Writer Identification Using Handwritten Cursive Texts and Single Character Words

DeepFuseOSV: online signature verification using hybrid feature fusion and depthwise separable convolution neural network architecture

Impact of Type of Convolution Operation on Performance of Convolutional Neural Networks for Online Signature Verification

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