Conference Paper2012Cited by 8

A comparative study of signature recognition problem using statistical features and artificial neural networks

Mahabub Akram, Romasa Qasim, M Ashraful Amin

2012 International Conference on Informatics, Electronics & Vision (ICIEV)

IEEE, pp. 925–929

DOI: 10.1109/iciev.2012.6317435URL

Abstract

This paper summarizes a research effort for an off-line signature recognition system. Neural network is used to address this problem because the learning and generalization abilities of NNs enable them to cope up with the diversity and the variation of human signatures. Since neural network have proven performance in other pattern recognition tasks such as character recognition therefore, it is equally suitable for the task of signature recognition. In this paper we present a comparative study of signature recognition comprises of three different neural networks i.e., feed-forward-back propagation neural network, competitive and probabilistic neural network. We have proved by experiment and analysis that probabilistic neural network is best suited to deal with signature recognition problem with an average of 100% accuracy.

Keywords

  • statistical features
  • Probabilistic neural network
  • feedforward backpropagation neural network
  • offline signature recognition
  • competitive neural networks
  • neural network comparison
  • handwritten signature verification

CCDS Authors

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

References

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Cited By

  1. 1.Kiran Bibi, Saeeda Naz, Arshia Rehman. (2019). Biometric signature authentication using machine learning techniques: Current trends, challenges and opportunities. Multimedia Tools and Applications, 79(1-2), 289–340[10.1007/s11042-019-08022-0]
  2. 2.Aini Najwa Azmi, Dewi Nasien, Fakhrul Syakirin Omar. (2016). Biometric signature verification system based on freeman chain code and k-nearest neighbor. Multimedia Tools and Applications, 76(14), 15341–15355[10.1007/s11042-016-3831-2]
  3. 3.Ahmed Remaida, Aniss Moumen, Younes El Bouzekri El Idrissi, Zineb Sabri. (2020). Handwriting Recognition with Artificial Neural Networks a Decade Literature Review. , 1–5[10.1145/3386723.3387884]
  4. 4.Nurullah Calik, Onur Can Kurban, Ali Riza Yilmaz, Lutfiye Durak Ata, Tulay Yildirim. (2017). Signature recognition application based on deep learning. , 1–4[10.1109/siu.2017.7960454]
  5. 5.Shubhangi L. Karanjkar, P. N. Vasambekar. (2016). Signature recognition on bank cheques using ANN. , 7, 44–47[10.1109/wiecon-ece.2016.8009084]
  6. 6.Aini Najwa Azmi, Dewi Nasien, Azurah Abu Samah. (2016). FREEMAN CHAIN CODE AS REPRESENTATION IN OFFLINE SIGNATURE VERIFICATION SYSTEM. Jurnal Teknologi, 78(8-2)[10.11113/jt.v78.9546]
  7. 7.Nadia Roosmalita Sari, Mohammad Zoqi Sarwani, Yudha Alif Aulia, Wayan Firdaus Mahmudy. (2018). Signature Pattern Recognition using Kohonen Network. Knowledge Engineering and Data Science, 1(2), 39–45[10.17977/um018v1i22018p39-45]
  8. 8.Divpreet singh, Ajay Mittal, Naveen Aggarwal, Rajesh Kumar. (2022). Offline Signature verification : A Systematic Review. Research Square[10.21203/rs.3.rs-1723015/v1]
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