Research advanced in offline handwritten signature verification

Research Article
Open access

Research advanced in offline handwritten signature verification

Yuhang Guo 1, Siyuan Li 2* Jinxuan Wu 3
  • 1 Xi’an Jiaotong-liverpool University    
  • 2 Shanghai University    
  • 3 Tongji University    
  • *corresponding author lyq@shu.edu.cn
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230653
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Biometrics technology has penetrated into various fields of our lives that require authentication and verification, such as criminal investigation, check processing and legal procedures, which is a focus of debate in the scientific research community. As a biometric feature that is easy to obtain, verification of handwritten signatures has attracted great interest in the past several decades. Although the online handwritten signature verification (OnHSV) system can obtain more information and has a higher accuracy rate, the offline handwritten signature verification (OfHSV) system remains the focus of research, since most signatures we can obtain are offline. In this paper, according to the difference of steps of OfHSV, we classify and introduce the advanced research work of OfHSV from four aspects: datasets, preprocessing techniques, feature extraction methods, and neural network structures. Additionally, we introduced the background of signature verification system and the experimental results of existing technologies. Finally, we presented some notable challenges, leading the reader to current trends and future directions in this field.

Keywords:

biometrics, offline handwritten signature verification, writer identification.

Guo,Y.;Li,S.;Wu,J. (2023). Research advanced in offline handwritten signature verification. Applied and Computational Engineering,6,1236-1244.
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References

[1]. Malik MI, Liwicki M, Ieee, editors. From Terminology to Evaluation: Performance Assessment of Automatic Signature Verification Systems. 13th International Conference on Frontiers in Handwriting Recognition (ICFHR); 2012 Sep 18-20; Monopoli, ITALY2012.

[2]. Kalera MK, Srihari S, Xu AH. Offline signature verification and identification using distance statistics. International Journal of Pattern Recognition and Artificial Intelligence. 2004;18(7):1339-60.

[3]. Pourshahabi MR, Sigari MH, Pourreza HR, editors. Offline Handwritten Signature Identification and Verification Using Contourlet Transform. International Conference of Soft Computing and Pattern Recognition; 2009 Dec 04-07; Malacca, MALAYSIA2009.

[4]. o-Khalifa O, Alam MK, Abdalla AH, editors. An Evaluation on Offline Signature Verification using Artificial Neural Network Approach. International Conference on Computer, Electrical and Electronics Engineering (ICCEEE); 2013 Aug 26-28; Khartoum, SUDAN2013.

[5]. Sharif M, Khan MA, Faisal M, Yasmin M, Fernandes SL. A framework for offline signature verification system: Best features selection approach. Pattern Recognition Letters. 2020;139:50-9.

[6]. Banerjee D, Chatterjee B, Bhowal P, Bhattacharyya T, Malakar S, Sarkar R. A new wrapper feature selection method for language-invariant offline signature verification. Expert Systems with Applications. 2021;186.

[7]. Batool FE, Attique M, Sharif M, Javed K, Nazir M, Abbasi AA, et al. Offline signature verification system: a novel technique of fusion of GLCM and geometric features using SVM. Multimedia Tools and Applications.

[8]. Shariatmadari S, Emadi S, Akbari Y. Patch-based offline signature verification using one-class hierarchical deep learning. International Journal on Document Analysis and Recognition. 2019;22(4):375-85.

[9]. Zois EN, Alexandridis A, Economou G. Writer independent offline signature verification based on asymmetric pixel relations and unrelated training-testing datasets. Expert Systems with Applications. 2019;125:14-32.

[10]. Janocha K, Czarnecki WM. On Loss Functions for Deep Neural Networks in Classification2017 February 01, 2017:[arXiv:1702.05659 p.].

[11]. Vo QN, Kim SH, Yang HJ, Lee G. Binarization of degraded document images based on hierarchical deep supervised network. Pattern Recognition. 2018;74:568-86.

[12]. Maergner P, Pondenkandath V, Alberti M, Liwicki M, Riesen K, Ingold R, et al. Combining graph edit distance and triplet networks for offline signature verification. Pattern Recognition Letters. 2019;125:527-33.

[13]. Calik N, Kurban OC, Yilmaz AR, Yildirim T, Ata LD. Large-scale offline signature recognition via deep neural networks and feature embedding. Neurocomputing. 2019;359:1-14.

[14]. Kao HH, Wen CY. An Offline Signature Verification and Forgery Detection Method Based on a Single Known Sample and an Explainable Deep Learning Approach. Applied Sciences-Basel. 2020;10(11).

[15]. Hafemann LG, Sabourin R, Oliveira LS. Meta-Learning for Fast Classifier Adaptation to New Users of Signature Verification Systems. Ieee Transactions on Information Forensics and Security. 2020;15:1735-45.

[16]. Justice D, Hero A. A binary linear programming formulation of the graph edit distance. Ieee Transactions on Pattern Analysis and Machine Intelligence. 2006;28(8):1200-14.

[17]. Riesen K, Bunke H. Approximate graph edit distance computation by means of bipartite graph matching. Image and Vision Computing. 2009;27(7):950-9.

[18]. Maergner P, Riesen K, Ingold R, Fischer A, Ieee, editors. A Structural Approach to Offline Signature Verification Using Graph Edit Distance. 14th IAPR International Conference on Document Analysis and Recognition (ICDAR); 2017 Nov 09-15; Kyoto, JAPAN2017.

Cite this article

Guo,Y.;Li,S.;Wu,J. (2023). Research advanced in offline handwritten signature verification. Applied and Computational Engineering,6,1236-1244.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Malik MI, Liwicki M, Ieee, editors. From Terminology to Evaluation: Performance Assessment of Automatic Signature Verification Systems. 13th International Conference on Frontiers in Handwriting Recognition (ICFHR); 2012 Sep 18-20; Monopoli, ITALY2012.

[2]. Kalera MK, Srihari S, Xu AH. Offline signature verification and identification using distance statistics. International Journal of Pattern Recognition and Artificial Intelligence. 2004;18(7):1339-60.

[3]. Pourshahabi MR, Sigari MH, Pourreza HR, editors. Offline Handwritten Signature Identification and Verification Using Contourlet Transform. International Conference of Soft Computing and Pattern Recognition; 2009 Dec 04-07; Malacca, MALAYSIA2009.

[4]. o-Khalifa O, Alam MK, Abdalla AH, editors. An Evaluation on Offline Signature Verification using Artificial Neural Network Approach. International Conference on Computer, Electrical and Electronics Engineering (ICCEEE); 2013 Aug 26-28; Khartoum, SUDAN2013.

[5]. Sharif M, Khan MA, Faisal M, Yasmin M, Fernandes SL. A framework for offline signature verification system: Best features selection approach. Pattern Recognition Letters. 2020;139:50-9.

[6]. Banerjee D, Chatterjee B, Bhowal P, Bhattacharyya T, Malakar S, Sarkar R. A new wrapper feature selection method for language-invariant offline signature verification. Expert Systems with Applications. 2021;186.

[7]. Batool FE, Attique M, Sharif M, Javed K, Nazir M, Abbasi AA, et al. Offline signature verification system: a novel technique of fusion of GLCM and geometric features using SVM. Multimedia Tools and Applications.

[8]. Shariatmadari S, Emadi S, Akbari Y. Patch-based offline signature verification using one-class hierarchical deep learning. International Journal on Document Analysis and Recognition. 2019;22(4):375-85.

[9]. Zois EN, Alexandridis A, Economou G. Writer independent offline signature verification based on asymmetric pixel relations and unrelated training-testing datasets. Expert Systems with Applications. 2019;125:14-32.

[10]. Janocha K, Czarnecki WM. On Loss Functions for Deep Neural Networks in Classification2017 February 01, 2017:[arXiv:1702.05659 p.].

[11]. Vo QN, Kim SH, Yang HJ, Lee G. Binarization of degraded document images based on hierarchical deep supervised network. Pattern Recognition. 2018;74:568-86.

[12]. Maergner P, Pondenkandath V, Alberti M, Liwicki M, Riesen K, Ingold R, et al. Combining graph edit distance and triplet networks for offline signature verification. Pattern Recognition Letters. 2019;125:527-33.

[13]. Calik N, Kurban OC, Yilmaz AR, Yildirim T, Ata LD. Large-scale offline signature recognition via deep neural networks and feature embedding. Neurocomputing. 2019;359:1-14.

[14]. Kao HH, Wen CY. An Offline Signature Verification and Forgery Detection Method Based on a Single Known Sample and an Explainable Deep Learning Approach. Applied Sciences-Basel. 2020;10(11).

[15]. Hafemann LG, Sabourin R, Oliveira LS. Meta-Learning for Fast Classifier Adaptation to New Users of Signature Verification Systems. Ieee Transactions on Information Forensics and Security. 2020;15:1735-45.

[16]. Justice D, Hero A. A binary linear programming formulation of the graph edit distance. Ieee Transactions on Pattern Analysis and Machine Intelligence. 2006;28(8):1200-14.

[17]. Riesen K, Bunke H. Approximate graph edit distance computation by means of bipartite graph matching. Image and Vision Computing. 2009;27(7):950-9.

[18]. Maergner P, Riesen K, Ingold R, Fischer A, Ieee, editors. A Structural Approach to Offline Signature Verification Using Graph Edit Distance. 14th IAPR International Conference on Document Analysis and Recognition (ICDAR); 2017 Nov 09-15; Kyoto, JAPAN2017.

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