Automated Offline Arabic Signature Verification System using Multiple Features Fusion for Forensic Applications

Darwish, Saad M.; El-Nour, Ashraf M.

doi:10.12816/0033136

Cited by 3 publications

(2 citation statements)

References 12 publications

(13 reference statements)

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“…Authors built their own database with genuine signatures from 10 volunteers. Additionally, a proposal based on multiple feature fusion with fuzzy modeling can be found in [42]. Similarly, in [164], a system was proposed based on various geo-metrical and grid features.…”

Section: Arabic Signature Verificationmentioning

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: Arabic Signature Verificationmentioning

confidence: 99%

A Perspective Analysis of Handwritten Signature Technology

Diaz¹,

et al. 2019

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“…Based on feature computation strategy several signature verifications tasks were reported in literature on non-Indic and Indic scripts. Global features like signature boundary, energy, projection information, size of signature box [4], geometric, centroid information [5], statistical texture measure [6], quad-tree structure [7], descriptor like SIFT [8], SURF [9], Discrete Wavelet Transform [10], geometric and grid based feature [11], feature fusion with fuzzy modeling [12], histogram of oriented gradients [13] were considered for signature verification from various non-Indic scripts. Among the various popular works on Indic scripts, features like Zernike moments and gradient [14], chain-code based contour information [15], local binary patterns [16] were considered.…”

Section: Introductionmentioning

confidence: 99%

SigVer—A Deep Learning Based Writer Independent Bangla Signature Verification System

Obaidullah

Mukherjee

Pal

2021

Communications in Computer and Information Science

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Handwritten signature is one of the most popular biometric traits which have been addressed by researchers since the last few decades. Scientists have proposed disparate handcrafted feature-based techniques for signature verification in the past. Presently there has been an interest towards deep learning-based approaches which has demonstrated promising results across different avenues of pattern recognition problems. In this paper, light-weight CNN architecture named SigVer is proposed for Bangla offline writer independent signature verification. The proposed network was built with only six layers with no pooling in between convolution layers to minimize information loss. Experiments were performed on 2956 signatures from 57 writers and a highest accuracy of 99.49% was obtained. Further, comparative analysis was performed with established CNN architectures as well as handcrafted feature-based techniques and SigVer produced better results.

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SEN: Stack Ensemble Shallow Convolution Neural Network for Signature-based Writer Identification

Obaidullah

Ghosh

Mukherjee

et al. 2022

2022 26th International Conference on Pattern Recognition (ICPR)

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Automated Offline Arabic Signature Verification System using Multiple Features Fusion for Forensic Applications

Abstract: Open Accesswriting process, it will be more difficult to obtain higher verification accuracy. This paper addresses the above difficulty by introducing a novel offline

Cited by 3 publications

References 12 publications

A Perspective Analysis of Handwritten Signature Technology

A Perspective Analysis of Handwritten Signature Technology

SigVer—A Deep Learning Based Writer Independent Bangla Signature Verification System

SEN: Stack Ensemble Shallow Convolution Neural Network for Signature-based Writer Identification

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