Human-Assisted Signature Recognition Based on Comparative Attributes

Morocho, Derlin; Morales, Aythami; Fiérrez, Julián; Vera-Rodríguez, Rubén

doi:10.1109/icdar.2017.373

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…Results obtained using CNN: Overall accuracy of 83.76% is obtained using CNN.Confusion matrix for CNN is given in Table II. [7] showed EER of 21.20%. Offline Signature Verification based on low level key strokes [6] showed EER of 15.59% whereas Offline Signature Recognition using Support Vector Machine [10] showed EER of 7.16%.…”

Section: Experimentalresultsmentioning

confidence: 99%

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Signature Verification Using Support Vector Machine and Convolution Neural Network

Vohra¹

2021

TURCOMAT

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Signature is used for recognition of an individual. Signature is considered as a mark that an individual write on a paper for his/her identity or proof. It is used as a unique feature for identifying an individual. It is highly used in social and business functions which gives rise to verification of signature. There are chances of signature getting forged. Hence, the need to identify signature as genuine of forged is utmost important. In this paper, identification of signature as genuine or forged is done using two approaches. First approach is using SVM and second is using CNN. For SVM, pre-processing of signature image is done and feature extraction is performed. Features extracted are histogram of gradient, shape, aspect ratio, bounding area, contour area and convex hull area. Further, SVM is applied to classify signature as genuine or forged and accuracy is determined. In the second approach, signature image is pre-processed, CNN is used to classify signature as genuine or forged and accuracy is determined. Dataset used here is ICDAR Dutch dataset along with 80 signatures taken from 4 people.Dutch dataset consists of 362 signature imagesand signature images taken from 4 people consists 10 genuine and 10 forged signatures which sums to 442 signature images. The proposed system provides accuracy of 86.39% using SVM and around 83.78% using CNN.

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Section: Experimentalresultsmentioning

confidence: 99%

“…ERR achieved in this research is 15.59%. Derlin Morocho et al [7] calculates the performance of correlative attributes forverification of signature. EER achieved ranges 5.5% to 21.2%.…”

Section: Introductionmentioning

confidence: 99%

Signature Verification Using Support Vector Machine and Convolution Neural Network

Vohra¹

2021

TURCOMAT

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“…Several studies [ 148 , [154] , [155] , [156] , [157] , [158] , [159] , [160] , [161] , [162] , [163] , [164] , [165] , [166] , [167] , [168] , [169] , [170] , [171] , [172] , [173] , [174] , [175] , [176] ] focused on the development and test of automatic systems to identify the writer of different available datasets. Some researches were based on deep learning systems and others used basic statistical tools to create a classification method.…”

Section: Handwriting/signaturementioning

confidence: 99%

“…Morocho D. and al [ 171 ] presented a new semiautomatic signature labelling interface inspired by FDE.…”

Section: Handwriting/signaturementioning

confidence: 99%

Interpol review of questioned documents 2016–2019

Deviterne-Lapeyre

2020

Forensic Science International: Synergy

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“…In general, it shows an individual characteristic of a person and is a personal attribute for authentication [3], [5]. Signatures have many applications and they are used in wide areas such as financial transactions, banking systems, cheques, insurance, access control, document authentication, signing contracts, work documents, petitions, corporations, hospitals, administrative issues, forensics cases, notary public, employees' attendance, exams, and signing of certifications [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Deep Study on the Performance of the Spatial Density Distribution Method to Recognize Handwritten Signatures

Kamal

George

Ameer

2022

eijs

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A signature is a special identifier that confirms a person's identity and distinguishes him or her from others. The main goal of this paper is to present a deep study of the spatial density distribution method and the effect of a mass-based segmentation algorithm on its performance while it is being used to recognize handwritten signatures in an offline mode. The methodology of the algorithm is based on dividing the image of the signature into tiles that reflect the shape and geometry of the signature, and then extracting five spatial features from each of these tiles. Features include the mass of each tile, the relative mean, and the relative standard deviation for the vertical and horizontal projections of that tile. In the classification stage, four measurements of the Euclidean distance were used. While the accuracy rates for 4854 samples drawn from five different evaluated standard datasets ranged from 92.24% to 100%.

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Human-Assisted Signature Recognition Based on Comparative Attributes

Cited by 10 publications

References 18 publications

Signature Verification Using Support Vector Machine and Convolution Neural Network

Signature Verification Using Support Vector Machine and Convolution Neural Network

Interpol review of questioned documents 2016–2019

A Deep Study on the Performance of the Spatial Density Distribution Method to Recognize Handwritten Signatures

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