Online Signature Authentication using Pre-trained Optimization Techniques
Keywords:
Conv Neural Network (CNN) model, VGG16, Xception, ResNet50, OptimizersAbstract
Authorization is essential in order to handle the document assurances and security. Nowadays it constitutes one of the top responsibilities in case of securing information and effectiveness in every domain. The development of technological advances has made interacting with machinery more effortless. As a result, the demand for authentication grows quickly for a variety of legitimate causes. Therefore, biometric-based identification has dramatically accelerated. It is a sort of improvement beyond various other approaches. The author presented Conv Neural Networks for mining features moreover supervised machine learning techniques for the verification of handwritten signatures. Raw images of signatures are used to train CNN models for extracting features along with data augmentation. CNN Architectures especially pre-trained models as VGG16, Inception-v3, ResNet50, and Xception are used for identifying signature either original or forgery. Using Euclidean distance, cosine similarity, and supervised learning algorithms such as Logistic Regression, Random Forest, SVM, and its derivatives, the extracted characteristics are categorized into two classes: authentic or fake. Data for testing is gathered from the ICDAR 2011 Signature Dataset and structured in pairs. The metadata consists of 69 subjects' signatures.
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J. A. P. Lopes, B. Baptista, N. Lavado, and M. Mendes, “Offline Handwritten Signature Verification Using Deep Neural Networks,” Energies, vol. 15, no. 20, pp. 1–15, 2022, doi: 10.3390/en15207611.
H. H. Kao and C. Y. Wen, “An offline signature verification and forgery detection method based on a single known sample and an explainable deep learning approach,” Applied Sciences (Switzerland), vol. 10, no. 11, 2020, doi: 10.3390/app10113716.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 580–587, 2014, doi: 10.1109/CVPR.2014.81.
D. Scherer, A. Müller, and S. Behnke, “Evaluation of pooling operations in convolutional architectures for object recognition,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6354 LNCS, no. PART 3, pp. 92–101, 2010, doi: 10.1007/978-3-642-15825-4_10.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-December, pp. 770–778, 2016, doi: 10.1109/CVPR.2016.90.
Z. Zhang, X. Liu, and Y. Cui, “Multi-phase offline signature verification system using deep convolutional generative adversarial networks,” Proceedings - 2016 9th International Symposium on Computational Intelligence and Design, ISCID 2016, vol. 2, pp. 103–107, 2016, doi: 10.1109/ISCID.2016.2033.
Jahandad, S. M. Sam, K. Kamardin, N. N. Amir Sjarif, and N. Mohamed, “Offline signature verification using deep learning convolutional Neural network (CNN) architectures GoogLeNet inception-v1 and inception-v3,” Procedia Computer Science, vol. 161, pp. 475–483, 2019, doi: 10.1016/j.procs.2019.11.147.
M. Hoseini and F. Mahmood, “Mohsen Fayyaz1, Mohammad Hajizadeh _ Saffar, Mohammad Sabokrou,” pp. 88–91, 2015.
and M. F. M. Fayyaz, M. H. Saffar, M. Sabokrou, M. Hoseini, “Online Signature Verification Based on Feature Representation,” in International Symposium on Artificial Intelligence and Signal Processing, Iran, Mashhad, 2015, 2015.
R. Ghosh, “A Recurrent Neural Network based deep learning model for offline signature verification and recognition system,” Expert Systems with Applications, vol. 168, p. 114249, 2021, doi: 10.1016/j.eswa.2020.114249.
and H. L. J. Kim, H. Kim, “Online signature verification using deep convolutional neural network,” in International Joint Conference on Neural Networks (IJCNN), 2016.
and J. K. H. Kim, H. Lee, “Online signature verification using deep neural network with hidden Markov model,” Journal of Ambient Intelligence and Humanized Computing, vol. 9, no. 3, pp. 191–202, 2018.
D. P. Sudharshan and R. N. Vismaya, “Handwritten Signature Verification System using Deep Learning,” IEEE International Conference on Data Science and Information System, ICDSIS 2022, pp. 39–44, 2022, doi: 10.1109/ICDSIS55133.2022.9915833.
J. Poddar, V. Parikh, and S. K. Bharti, “Offline Signature Recognition and Forgery Detection using Deep Learning,” Procedia Computer Science, vol. 170, no. 2019, pp. 610–617, 2020, doi: 10.1016/j.procs.2020.03.133.
D. Menotti et al., “Deep Representations for Iris, Face, and Fingerprint Spoofing Detection,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 4, pp. 864–879, 2015, doi: 10.1109/TIFS.2015.2398817.
T. O. Oladele, K. S. Adewole, and A. O. Oyelami, “Forged Signature Detection Using Artificial Neural Network,” African Journal of Computing & ICT, vol. 7, no. 3, pp. 11–20, 2014.
and X. Z. L. Zhang, X. Li, “Signature verification using convolutional neural network,” in International Conference on Pattern Recognition (ICPR), p. 2016.
S. B. S. and R. S. B. Sadkhan, “Analysis of Different Types of Digital Signature,” in 8th International Engineering Conference on Sustainable Technology and Development (IEC), Erbil, Iraq, 2022, pp. 241–246.
S. Minaee, A. Abdolrashidi, H. Su, M. Bennamoun, and D. Zhang, “Biometrics recognition using deep learning: a survey,” Artificial Intelligence Review, 2023, doi: 10.1007/s10462-022-10237-x.
S. X. and Y. Z. T. Yang, Y. Zhang, “Digital signature based on ISRSAC,” 2021, vol. 18, no. 1.
J. Malik, A. Elhayek, S. Guha, S. Ahmed, A. Gillani, and D. Stricker, “Deepairsig: End-to-end deep learning based in-air signature verification,” IEEE Access, vol. 8, pp. 195832–195843, 2020, doi: 10.1109/ACCESS.2020.3033848.
L. G. Hafemann, R. Sabourin, and L. S. Oliveira, “Learning features for offline handwritten signature verification using deep convolutional neural networks,” Pattern Recognition, vol. 70, pp. 163–176, 2017, doi: 10.1016/j.patcog.2017.05.012.
G. . P. Diaz, M.; Ferrer, M.A.; Impedovo, D.; Malik, M.I.; Pirlo, “A perspective analysis of handwritten signature technology,” ACM Comput. Surv, vol. 51, pp. 1–39, 2019.
H.-R. Deng, P.S.; Liao, H.-Y.M.; Ho, C.W.; Tyan, “Wavelet-based online handwritten signature verification,” Comput. Vis. Image Underst, vol. 76, pp. 173–190, 1998.
M. Pal, S.; Alaei, A.; Pal, U.; Blumenstein, “Performance of an online signature verification method based on texture features on a large indic-script signature dataset,” in In Proceedings of the 2016 12th IAPR Workshop on Document Analysis Systems (DAS), Santorini, Greece, 2016.
R. P. P. Fathimathul et al., “A Novel Method for the Classification of Butterfly Species Using Pre-Trained CNN Models,” Electronics (Switzerland), vol. 11, no. 13, pp. 1–20, 2022, doi: 10.3390/electronics11132016.
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