On-line signature verification based on forward and backward variances of signature

Wibowo, Canggih Puspo; Thumwarin, P.; Matsuura, T.

doi:10.1109/jictee.2014.6804069

Cited by 3 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The attention on geometrical and temporal features like writing velocity or acceleration was attracted by References [27][28][29][30][31]. It is known that signatures written by the same person differ slightly from time to time.…”

Section: Of 25mentioning

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%

See 1 more Smart Citation

Writer Identification Using Handwritten Cursive Texts and Single Character Words

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Of 25mentioning

confidence: 99%

Section: Of 25mentioning

confidence: 99%

Writer Identification Using Handwritten Cursive Texts and Single Character Words

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Smejkal, Vladimir, and Jindrich Kodl [9], Liu, Yishu, Zhihua rule, and Lihua rule [10], Ribeiro, Bernardete, Noel Lopes, and Joao Goncalves [11], and López-García, Mariano, et al [12] approached to a additional real time implementation with the employment of techniques like GPU, embedded systems and alternative hardwares. Wibowo, Canggih Puspo, Pitak Thumwarin, and Takenobu Matsuura [14] projected a replacement options referred to as the forward and backward variances of signature for on-line signature verification. At the most recent, Sae-Bae, Napa, and Nasir Memon [15] evolved a method that uses position similarly as pressure terms for secure guide that uses a mixture of 1D and 2nd histograms.…”

Section: Literature Reviewmentioning

confidence: 99%

Online Signature Verification System on Mobile Devices for Effective and Secure Biometric

Singh¹,

Kaur²

2017

EIJSE

View full text Add to dashboard Cite

Handwritten signature is that the most generally accepted biometric to biometric identification. The projected on-line written signature verification system consists principally of 3 phases: Signal preprocessing, feature extraction, and have matching. Steps for confirming on-line written signature during this system begin with extracting dynamic knowledge (x and y positions) of points that forming the signature. Pen-movement angles and speed square measure then derived from pen position knowledge. To scale back variations in pen-position and pen-movement angles spatial property, knowledge is normalized. Options of the signature will be extracted victimization projected feature extraction methodology. Such as each signature a novel feature are extracted and this can be amount victimization quantization step size vector. To verify the check signature Manhattan distance (score) are taken between the check signature and therefore the reference signature. If score is a smaller amount than the predefined threshold then the check signature same is claimed to be real signature and if score is over the predefined threshold then it's said to be cast signature.

show abstract

Text-dependent User Verification of Handwritten Words and Signatures on Mobile Devices

Guerin

Vidal

Macchiavello

2015

The Computer Journal

View full text Add to dashboard Cite

On-line signature verification based on forward and backward variances of signature

Cited by 3 publications

References 6 publications

Writer Identification Using Handwritten Cursive Texts and Single Character Words

Writer Identification Using Handwritten Cursive Texts and Single Character Words

Online Signature Verification System on Mobile Devices for Effective and Secure Biometric

Text-dependent User Verification of Handwritten Words and Signatures on Mobile Devices

Contact Info

Product

Resources

About