Enhancing Offline Signature Verification through CNN Model Optimization with PSO Algorithm
Keywords:
Offline Signature Verification, Deep Learning, Particle Swarm Optimization Algorithm and Convolutional Neural Network.Abstract
The verification of handwritten signatures is integral to numerous applications such as authentication and document verification. The efficacy of an offline signature verification system relies heavily on the feature extraction stage, because it significantly affects the performance of the system. Both the quality and quantity of extracted features play pivotal roles in enabling the system to distinguish between genuine and forged signatures. In this study, we introduce a novel approach aimed at optimizing the hyperparameters of a Convolutional Neural Network (CNN) model for handwritten signature verification by leveraging a Particle Swarm Optimization (PSO) algorithm. The PSO algorithm, inspired by the flocking behavior of birds, is a population-based optimization method. We delineated a search space encompassing various hyperparameter ranges, including the number of convolutional filters, dense layers, dropout rate, and learning rate. Through iterative updates to the positions and velocities of the particles, the PSO algorithm navigates this search space to identify the optimal set of hyperparameters that maximizes the accuracy of the CNN model. Our approach was evaluated across diverse datasets including BHSig260-Bengali, BHSig260-Hindi, GPDS, and CEDAR, each containing a varied assortment of handwritten signature images. The experimental results demonstrate the effectiveness of our proposed method, achieving a remarkable accuracy of 98.3% on the testing dataset.
Downloads
References
K. Jain, K. Nandakumar, and A. Ross, “50 years of biometric research: Accomplishments, challenges, and opportunities,” Pattern Recognit Lett, vol. 79, pp. 80–105, Aug. 2016, doi: 10.1016/j.patrec.2015.12.013.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/nature14539.
Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks.” [Online]. Available: http://code.google.com/p/cuda-convnet/
F. Hutter, H. H. Hoos, and K. Leyton-Brown, “Sequential Model-Based Optimization for General Algorithm Configuration,” 2011, pp. 507–523. doi: 10.1007/978-3-642-25566-3_40.
Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), IEEE, 1998, pp. 69–73. doi: 10.1109/ICEC.1998.699146.
X. Zhang, Z. Wu, L. Xie, Y. Li, F. Li, and J. Zhang, “Multi-Path Siamese Convolution Network for Offline Handwritten Signature Verification,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Jan. 2022, pp. 51–58. doi: 10.1145/3512850.3512854.
F. C. Soon, H. Y. Khaw, J. H. Chuah, and J. Kanesan, “Hyper‐parameters optimisation of deep CNN architecture for vehicle logo recognition,” IET Intelligent Transport Systems, vol. 12, no. 8, pp. 939–946, Oct. 2018, doi: 10.1049/iet-its.2018.5127.
Wang, Y. Sun, B. Xue, and M. Zhang, “Evolving Deep Convolutional Neural Networks by Variable-Length Particle Swarm Optimization for Image Classification,” in 2018 IEEE Congress on Evolutionary Computation (CEC), IEEE, Jul. 2018, pp. 1–8. doi: 10.1109/CEC.2018.8477735.
S. S. Talathi, “Hyper-parameter optimization of deep convolutional networks for object recognition,” in 2015 IEEE International Conference on Image Processing (ICIP), IEEE, Sep. 2015, pp. 3982–3986. doi: 10.1109/ICIP.2015.7351553.
S. Y. S. Leung, Y. Tang, and W. K. Wong, “A hybrid particle swarm optimization and its application in neural networks,” Expert Syst Appl, vol. 39, no. 1, pp. 395–405, Jan. 2012, doi: 10.1016/j.eswa.2011.07.028.
E. Camci, D. R. Kripalani, L. Ma, E. Kayacan, and M. A. Khanesar, “An aerial robot for rice farm quality inspection with type-2 fuzzy neural networks tuned by particle swarm optimization-sliding mode control hybrid algorithm,” Swarm Evol Comput, vol. 41, pp. 1–8, Aug. 2018, doi: 10.1016/j.swevo.2017.10.003.
J. Raitoharju, S. Kiranyaz, and M. Gabbouj, “Training Radial Basis Function Neural Networks for Classification via Class-Specific Clustering,” IEEE Trans Neural Netw Learn Syst, vol. 27, no. 12, pp. 2458–2471, Dec. 2016, doi: 10.1109/TNNLS.2015.2497286.
R. Plamondon and S. N. Srihari, “Online and off-line handwriting recognition: a comprehensive survey,” IEEE Trans Pattern Anal Mach Intell, vol. 22, no. 1, pp. 63–84, 2000, doi: 10.1109/34.824821.
Tsourounis, I. Theodorakopoulos, E. N. Zois, and G. Economou, “From text to signatures: Knowledge transfer for efficient deep feature learning in offline signature verification,” Expert Syst Appl, vol. 189, p. 116136, Mar. 2022, doi: 10.1016/j.eswa.2021.116136.
J. Zheng et al., “DWSCNN Online Signature Verification Algorithm Based on CAE-MV Feature Dimensionality Reduction,” IEEE Access, vol. 12, pp. 22144–22157, 2024, doi: 10.1109/ACCESS.2024.3355449.
T. Longjam, D. R. Kisku, and P. Gupta, “Writer independent handwritten signature verification on multi-scripted signatures using hybrid CNN-BiLSTM: A novel approach,” Expert Syst Appl, vol. 214, p. 119111, Mar. 2023, doi: 10.1016/j.eswa.2022.119111.
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Sep. 2014, [Online]. Available: http://arxiv.org/abs/1409.1556
S. Masoudnia, O. Mersa, B. N. Araabi, A.-H. Vahabie, M. Amin Sadeghi, and M. N. Ahmadabadi, “Multi-Representational Learning for Offline Signature Verification using Multi-Loss Snapshot Ensemble of CNNs.”
Christianto, J. Colin, and G. P. Kusuma, “International Journal of Computing and Digital Systems Authentic Signature Verification Using Deep Learning Embedding With Triplet Loss Optimization And Machine Learning Classification.” [Online]. Available: http://journals.uob.edu.bh
T. Sinha, A. Haidar, and B. Verma, “Particle Swarm Optimization Based Approach for Finding Optimal Values of Convolutional Neural Network Parameters,” in 2018 IEEE Congress on Evolutionary Computation (CEC), IEEE, Jul. 2018, pp. 1–6. doi: 10.1109/CEC.2018.8477728.
H.-G. Han, W. Lu, Y. Hou, and J.-F. Qiao, “An Adaptive-PSO-Based Self-Organizing RBF Neural Network,” IEEE Trans Neural Netw Learn Syst, vol. 29, no. 1, pp. 104–117, Jan. 2018, doi: 10.1109/TNNLS.2016.2616413.
J.-F. Qiao, C. Lu, and W.-J. Li, “Design of Dynamic Modular Neural Network Based on Adaptive Particle Swarm Optimization Algorithm,” IEEE Access, vol. 6, pp. 10850–10857, 2018, doi: 10.1109/ACCESS.2018.2803084.
M. A. Ferrer, J. F. Vargas, A. Morales, and A. Ordonez, “Robustness of Offline Signature Verification Based on Gray Level Features,” IEEE Transactions on Information Forensics and Security, vol. 7, no. 3, pp. 966–977, Jun. 2012, doi: 10.1109/TIFS.2012.2190281.
Y. Wang, H. Zhang, and G. Zhang, “cPSO-CNN: An efficient PSO-based algorithm for fine-tuning hyper-parameters of convolutional neural networks,” Swarm Evol Comput, vol. 49, pp. 114–123, Sep. 2019, doi: 10.1016/j.swevo.2019.06.002.
S. S. and A. X. MEENAKSHI K. KALERA, “OFFLINE SIGNATURE VERIFICATION AND IDENTIFICATION USING DISTANCE STATISTICS,” Intern J Pattern Recognit Artif Intell, vol. 18, no. 07, p. . 1339-1360, 2004.
M. A. Ferrer, M. Diaz-Cabrera, and A. Morales, “Static Signature Synthesis: A Neuromotor Inspired Approach for Biometrics,” IEEE Trans Pattern Anal Mach Intell, vol. 37, no. 3, pp. 667–680, Mar. 2015, doi: 10.1109/TPAMI.2014.2343981.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition.” [Online]. Available: http://image-net.org/challenges/LSVRC/2015/
S. Pal, A. Alaei, U. Pal, and M. Blumenstein, “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), IEEE, Apr. 2016, pp. 72–77. doi: 10.1109/DAS.2016.48.
S. Dey, A. Dutta, J. I. Toledo, S. K. Ghosh, J. Llados, and U. Pal, “SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification,” Jul. 2017, [Online]. Available: http://arxiv.org/abs/1707.02131
W. Xiao and Y. Ding, “A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification,” Symmetry (Basel), vol. 14, no. 6, Jun. 2022, doi: 10.3390/sym14061216.
R. Kumar, J. D. Sharma, and B. Chanda, “Writer-independent off-line signature verification using surroundedness feature,” Pattern Recognit Lett, vol. 33, no. 3, pp. 301–308, Feb. 2012, doi: 10.1016/j.patrec.2011.10.009.
Dutta, U. Pal, and J. Llados, “Compact correlated features for writer independent signature verification,” in 2016 23rd International Conference on Pattern Recognition (ICPR), IEEE, Dec. 2016, pp. 3422–3427. doi: 10.1109/ICPR.2016.7900163.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
