A study on the consistency and significance of local features in off-line signature verification

Kővári, Bence; Charaf, Hassan

doi:10.1016/j.patrec.2012.10.011

Cited by 17 publications

(8 citation statements)

References 17 publications

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“…At the preprocessing stage, the aim is to enhance the signature through image and signal processing techniques for static and dynamic signatures. At the feature extraction stage, the techniques can be divided into function-based features, which use the timing information in the signature; global parameters, which describe particular aspects of a signature (e.g., loops or baselines [142]); component-oriented features, which focus on particular characteristics, such as a pen-up or pen-down; and pixel-based techniques. At the verification stage, various strategies, some quite classical, are described in the recent literature, for example, dynamic time warping (DTW) [52,82,83,102,140,176,[234][235][236] or support vector machines (SVMs) [54,56,76,104,198,274].…”

Section: Automatic Signature Verification: a Summary Of Best Practicesmentioning

confidence: 99%

A Perspective Analysis of Handwritten Signature Technology

Diaz¹,

et al. 2019

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Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear witness. Ever since, and over the last 10 years, the application of handwritten signature technology has strongly evolved and much research has focused on the possibility of applying systems based on handwritten signature analysis and processing to a multitude of new fields. After several years of haphazard growth of this research area, it is time to assess its current developments for their applicability in order to draw a structured way forward. This perspective reports a systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject. CCS Concepts: • General and reference → Surveys and overviews; • Security and privacy → Biometrics; Access control; • Theory of computation → Pattern matching; • Applied computing → Document analysis; System forensics;

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Section: Automatic Signature Verification: a Summary Of Best Practicesmentioning

confidence: 99%

A Perspective Analysis of Handwritten Signature Technology

Diaz¹,

et al. 2019

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“…FAR should be avoided in practical applications while the FRR must be tolerated. To deal with the FAR, the system must be tested against various classes of counterfeiting [12].…”

Section: ) Global Featuresmentioning

confidence: 99%

Component-connected Feature for Signature Identification

Umniati

Mutiara

Kusuma

et al. 2018

International Journal on Advanced Science, Engineering and Information Technology

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Abstract-A signature is the oldest security techniques to verify the identification of a person. This is due to every person has a different signature, and each signature has the characteristic physiological and behavior. There are two kinds of signature such as offline and online signatures used to verify someone identity. Offline signatures were used in this study because offline signature does not have dynamic features such as an online signature. This study proposed an identification system of offline signature by using k-NN based on the features that were stored in the database. The proposed identification system consists of preprocessing, feature extraction and verification stages. We collected the data samples from 10 persons. Each person wrote ten signatures. Total data was 100 signatures. The first stage used in this study was preprocessing such as noise removal, binarization, skeleton, and cropping. The second stage was feature extraction. Feature extraction had some vital information such as height-width ratio, the ratio of the density of signatures, edge distance ratio, the ratio of the number and proximity of the column, and the number of connected components in the signature. That information was stored in a separate database. We separated ten signatures of each person into six signatures as data sample and four signature as test data. We verified 40 signatures of test data from 10 persons using k-NN. It is shown that from 40 signatures used in our test data, 28 signatures were correctly identified and 12 signatures belong to others.

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“…The dataset used in our experiments is free and publicly available [7]. It consists of 4000 annotated samples from which 800 are forgeries.…”

Section: 1mentioning

confidence: 99%

Machine Learning Techniques for Detecting False Signatures

Teletin

2017

Studia UBB Informatica

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Abstract. Deciding whether a handwritten signature is legit or it has been falsified is a very complex task. Several methods have been tried out by the graphology experts in order to detect such fraud. However, it is obvious that it is very hard to perform such a classification. In this paper we investigate the possibility to use some supervised learning techniques in order to build models capable to accurately perform such an analysis. The results reported during the testing phase of the obtained model are encouraging for further work.

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A study on the consistency and significance of local features in off-line signature verification

Cited by 17 publications

References 17 publications

A Perspective Analysis of Handwritten Signature Technology

A Perspective Analysis of Handwritten Signature Technology

Component-connected Feature for Signature Identification

Machine Learning Techniques for Detecting False Signatures

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