Top

Neural Computing and Applications

Published in:

10-11-2020 | Original Article

Signature verification using geometrical features and artificial neural network classifier

Authors: Anamika Jain, Satish Kumar Singh, Krishna Pratap Singh

Published in: Neural Computing and Applications | Issue 12/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Signature verification has been one of the major researched areas in the field of computer vision. Many financial and legal organizations use signature verification as an access control and authentication. Signature images are not rich in texture; however, they have much vital geometrical information. Through this work, we have proposed a signature verification methodology that is simple yet effective. The technique presented in this paper harnesses the geometrical features of a signature image like center, isolated points, connected components, etc. and, with the power of artificial neural network classifier, classifies the signature image based on their geometrical features. Publicly available dataset MCYT, BHSig260 (contains the image of two regional languages Bengali and Hindi) has been used in this paper to test the effectiveness of the proposed method. We have received a lower equal error rate on MCYT 100 dataset and higher accuracy on the BHSig260 dataset.

previous article Homoclinic and heteroclinic motions of delayed inertial neural networks

next article CSCF: a chaotic sine cosine firefly algorithm for practical application problems

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Alam MK, Abdalla AH, et al. (2013) An evaluation on offline signature verification using artificial neural network approach. In: 2013 international conference on computing, electrical and electronic engineering (ICCEEE), pp 368–371. IEEE

Baltzakis H, Papamarkos N (2001) A new signature verification technique based on a two-stage neural network classifier. Eng Appl Artif Intel 14(1):95–103. https://doi.org/10.1016/S0952-1976(00)00064-6CrossRef

Cha SH, Srihari SN (2000) Writer identification: statistical analysis and dichotomizer. In: Joint IAPR international workshops on statistical techniques in pattern recognition (SPR) and structural and syntactic pattern recognition (SSPR), pp 123–132. Springer

Chandra S, Maheskar S (2016) Offline signature verification based on geometric feature extraction using artificial neural network. In: 2016 3rd international conference on recent advances in information technology (RAIT), pp 410–414. IEEE

Delaunay B, et al. (1934) Sur la sphere vide. Izv. Akad. Nauk SSSR, Otdelenie Matematicheskii i Estestvennyka Nauk 7(793-800), 1–2

Dey S, Dutta A, Toledo JI, Ghosh SK, Lladós J, Pal U (2017) Signet: convolutional siamese network for writer independent offline signature verification. CoRR. arXiv:1707.02131

Do MN (2001) Directional multiresolution image representations. Department of Communication Systems, Swiss Federal Institute of Technology Lausanne Ph.D. thesis

Dutta A, Pal U, Lladós J (2016) Compact correlated features for writer independent signature verification. In: 2016 23rd international conference on pattern recognition (ICPR), pp 3422–3427. IEEE

Dutta A, Pal U, Lladós J (2016) Compact correlated features for writer independent signature verification. In: 2016 23rd international conference on pattern recognition (ICPR), pp 3422–3427. https://doi.org/10.1109/ICPR.2016.7900163

10.

Eskander GS, Sabourin R, Granger E (2013) Hybrid writer-independent-writer-dependent offline signature verification system. IET Biometrics 2(4):169–181CrossRef

11.

Ferrer MA, Diaz-Cabrera M, Morales A (2013) Synthetic off-line signature image generation. In: 2013 international conference on biometrics (ICB), pp 1–7. IEEE

12.

Ferrer MA, Diaz-Cabrera M, Morales A (2014) Static signature synthesis: a neuromotor inspired approach for biometrics. IEEE Trans Pattern Anal Mach Intell 37(3):667–680CrossRef

13.

Ferrer MA, Vargas JF, Morales A, Ordonez A (2012) Robustness of offline signature verification based on gray level features. IEEE Trans Inf Forensics Secur 7(3):966–977. https://doi.org/10.1109/TIFS.2012.2190281CrossRef

14.

Fierrez-Aguilar J, Nanni L, Lopez-Peñalba J, Ortega-Garcia J, Maltoni D (2005) An on-line signature verification system based on fusion of local and global information. In: Kanade T, Jain A, Ratha NK (eds) Audio- and video-based biometric person authentication. Springer, Berlin, pp 523–532CrossRef

15.

Hafemann LG, Oliveira LS, Sabourin R (2018) Fixed-sized representation learning from offline handwritten signatures of different sizes. Int J Doc Anal Recognit (IJDAR) 21(3):219–232. https://doi.org/10.1007/s10032-018-0301-6CrossRef

16.

Hafemann LG, Sabourin R, Oliveira LS (2016) Analyzing features learned for offline signature verification using deep cnns. In: 2016 23rd international conference on pattern recognition (ICPR), pp 2989–2994. IEEE

17.

Hafemann LG, Sabourin R, Oliveira LS (2016) Writer-independent feature learning for offline signature verification using deep convolutional neural networks. In: 2016 international joint conference on neural networks (IJCNN), pp 2576–2583. IEEE

18.

Hafemann LG, Sabourin R, Oliveira LS (2017) Learning features for offline handwritten signature verification using deep convolutional neural networks. Pattern Recognit 70:163–176CrossRef

19.

Hamadene A, Chibani Y (2016) One-class writer-independent offline signature verification using feature dissimilarity thresholding. IEEE Trans Inf Forensics Secur 11(6):1226–1238CrossRef

20.

He L, Tan H, Huang ZC (2019) Online handwritten signature verification based on association of curvature and torsion feature with hausdorff distance. Multimed Tools Appl 78:19253–19278CrossRef

21.

Impedovo D, Pirlo G (2008) Automatic signature verification: the state of the art. IEEE Trans Syst Man Cybern Part C (Appl Rev) 38(5):609–635. https://doi.org/10.1109/TSMCC.2008.923866CrossRef

22.

Jain AK, Nandakumar K, Ross A (2016) 50 years of biometric research: accomplishments, challenges, and opportunities. Pattern Recognit Lett 79:80–105CrossRef

23.

Jain AK, Nandakumar K, Ross A (2016) 50 years of biometric research: accomplishments, challenges, and opportunities. Pattern Recognit Lett 79:80–105. https://doi.org/10.1016/j.patrec.2015.12.013CrossRef

24.

Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Trans Circuits Syst Video Technol 14(1):4–20CrossRef

25.

Karouni A, Daya B, Bahlak S (2011) Offline signature recognition using neural networks approach. Procedia Comput Sci 3:155–161CrossRef

26.

Lai S, Jin L, Yang W (2017) Online signature verification using recurrent neural network and length-normalized path signature descriptor. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), vol 1, pp 400–405. IEEE

27.

Landgrebe D (2000) On the relationship between class definition precision and classification accuracy in hyperspectral analysis. In: IGARSS 2000. IEEE 2000 international geoscience and remote sensing symposium. Taking the pulse of the planet: the role of remote sensing in managing the environment. Proceedings (Cat. No. 00CH37120), vol 1, pp 147–149. IEEE

28.

Manjunatha K, Manjunath S, Guru D, Somashekara M (2016) Online signature verification based on writer dependent features and classifiers. Pattern Recognit Lett 80:129–136CrossRef

29.

Maron ME (1961) Automatic indexing: an experimental inquiry. J ACM (JACM) 8(3):404–417CrossRef

30.

Møller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6(4):525–533CrossRef

31.

Okawa M (2018) Synergy of foreground background images for feature extraction: offline signature verification using fisher vector with fused kaze features. Pattern Recognit 79:480–489. https://doi.org/10.1016/j.patcog.2018.02.027CrossRef

32.

Ortega-Garcia J, Fierrez-Aguilar J, Simon D, Gonzalez J, Faundez-Zanuy M, Espinosa V, Satue A, Hernaez I, Igarza JJ, Vivaracho C et al (2003) Mcyt baseline corpus: a bimodal biometric database. IEE Proc-Vis Image Signal Process 150(6):395–401CrossRef

33.

Pal S, Alaei A, Pal U, Blumenstein M (2016) Performance of an off-line signature verification method based on texture features on a large indic-script signature dataset. In: 2016 12th IAPR workshop on document analysis systems (DAS), pp 72–77. IEEE

34.

Sadeghi J, Sadeghi S, Niaki STA (2014) Optimizing a hybrid vendor-managed inventory and transportation problem with fuzzy demand: an improved particle swarm optimization algorithm. Inf Sci 272:126–144MathSciNetCrossRef

35.

Sae-Bae N, Memon N (2014) Online signature verification on mobile devices. IEEE Trans Inf Forensics Secur 9(6):933–947. https://doi.org/10.1109/TIFS.2014.2316472CrossRef

36.

Saini P, Uniyal I, Singh N (2018) Offline graphical analysis of signatures using geometric features and artificial neural network. In: Intelligent communication, control and devices. Springer, pp 1065–1077

37.

Schapire RE (2003) The boosting approach to machine learning: an overview. In: Nonlinear estimation and classification. Springer, pp 149–171

38.

Shah AS, Khan M, Subhan F, Fayaz M, Shah A (2016) An offline signature verification technique using pixels intensity levels. Int J Signal Process Image Process Pattern Recognit 9(8):205–222

39.

Sharif M, Khan MA, Faisal M, Yasmin M, Fernandes SL (2018) A framework for offline signature verification system: best features selection approach. Pattern Recognit Lett. https://doi.org/10.1016/j.patrec.2018.01.021CrossRef

40.

Sharma A, Sundaram S (2017) A novel online signature verification system based on gmm features in a dtw framework. IEEE Trans Inf Forensics Secur 12(3):705–718. https://doi.org/10.1109/TIFS.2016.2632063CrossRef

41.

Soleimani A, Araabi BN, Fouladi K (2016) Deep multitask metric learning for offline signature verification. Pattern Recognit Lett 80:84–90CrossRef

42.

Suryani D, Irwansyah E, Chindra R (2017) Offline signature recognition and verification system using efficient fuzzy kohonen clustering network (efkcn) algorithm. Procedia Comput Sci 116:621–628CrossRef

43.

Tieu K, Viola P (2004) Boosting image retrieval. Int J Comput Vis 56(1–2):17–36CrossRef

44.

Yang L, Widjaja B, Prasad R (1995) Application of hidden Markov models for signature verification. Pattern Recognit 28(2):161–170. https://doi.org/10.1016/0031-3203(94)00092-ZCrossRef

45.

Zois EN, Alexandridis A, Economou G (2019) Writer independent offline signature verification based on asymmetric pixel relations and unrelated training-testing datasets. Expert Syst Appl 125:14–32CrossRef

46.

Zois EN, Theodorakopoulos I, Economou G (2017) Offline handwritten signature modeling and verification based on archetypal analysis. In: Proceedings of the IEEE international conference on computer vision, pp 5514–5523

Title: Signature verification using geometrical features and artificial neural network classifier
Authors: Anamika Jain
Satish Kumar Singh
Krishna Pratap Singh
Publication date: 10-11-2020
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 12/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05473-7

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 12/2021

Spatiotemporal dynamic of a coupled neutral-type neural network with time delay and diffusion

Optimal placement and sizing of FACTS devices for optimal power flow in a wind power integrated electrical network

Classification of operation cases in electric arc welding wachine by using deep convolutional neural networks

Machine learning models for ecological footprint prediction based on energy parameters

The Mode-Fisher pooling for time complexity optimization in deep convolutional neural networks

Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search

Premium Partner