Recognizing Signatures Using Normalized Generalization Neural Network
DOI:
https://doi.org/10.31272/jeasd.28.5.6Keywords:
Behavioural Biometric, Feature Extraction, Neural Network, Signature RecognitionAbstract
One of the most prevalent behavioral biometrics is the signature. In this paper, signatures are utilized in the case of recognition. Multiple contributions are provided here. Firstly, statistical analysis of efficiency is taken into consideration for the feature extraction. Secondly, a novel classifier is suggested. It is employed to recognize the signatures and it is called the Normalized Generalization Neural Network (NGNN). In terms of error rates, comparisons are established between different neural networks in the literature and the novel NGNN. The proposed NGNN consists of the input layer, normalization layer, Radial Basis Function (RBF) layer, and output layer. It can be considered as an enhanced or developed version of the Generalized Regression Neural Network (GRNN). A large number of signatures' attributes from the Biometric Ideal Test (BIT) database is utilized. That is, 1750 patterns of attributes are exploited. A significant improvement in the error rates over previous networks is achieved when using the novel NGNN. The Mean Absolute Error (MAE) has reached 0.028 and the Mean Square Error (MSE) has obtained 0.014. In addition, further experimental results on the BIT database showed better Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) of 0.002 and 0.119, respectively.
References
R. R. O. Al-Nima, “Signal processing and machine learning techniques for human verification based on finger textures,” Ph.D. thesis, Newcastle University, United Kingdom. 2017. URI: https://hdl.handle.net/10443/3982
S. A. M. Al-Sumaidaee, M. A. M. Abdullah, R. R. O. Al-Nima, S. S. Dlay, and J. A. Chambers, "Multi-gradient features and elongated quinary pattern encoding for image-based facial expression recognition," Pattern Recognition, vol. 71, pp. 249-263, Nov. 2017, https://dx.doi.org/10.1016/j.patcog.2017.06.007
S. A. M. Al-Sumaidaee, M. A. M. Abdullah, R. R. O. Al-Nima, S. S. Dlay, and J. A. Chambers, “Spatio-temporal modeling with multi-gradient features and elongated quinary pattern descriptor for dynamic facial expression recognition," Pattern Recognition, vol. 142, pp. 109647, Oct. 2023, https://dx.doi.org/10.1016/j.patcog.2023.109647
S. A. M. Al-Sumaidaee, S. S. Dlay, W. L. Woo, and J. A. Chambers, “Facial expression recognition using local gabor gradient code-horizontal diagonal descriptor,” in 2nd IET International Conference on Intelligent Signal Processing (ISP), 2015, pp. 1-6, https://dx.doi.org/10.1049/cp.2015.1766
E. Winarno, I. H. A. Amin, S. Hartati, and P. W. Adi, “Face Recognition based on CNN 2D-3D Reconstruction using Shape and Texture Vectors Combining,” Indonesian Journal of Electrical Engineering and Informatics (IJEEI), vol. 8, issue 2, pp. 378-384, June 2020, http://dx.doi.org/10.11591/ijeei.v8i2.1369
W. R. Abdulhussien, N. K. El Abbadi and A. M. Gaber, “Hybrid Deep Neural Network for Facial Expressions Recognition,” Indonesian Journal of Electrical Engineering and Informatics (IJEEI), vol. 9, issue 4, pp. 993-1007, Dec. 2021, https://dx.doi.org/10.52549/ijeei.v9i4.3425
M. Sharkas, “Ear recognition with ensemble classifiers; A deep learning approach,” Multimedia Tools and Applications, vol. 81, issue 30, pp. 43919–43945, May 2022, http://dx.doi.org/10.1007/s11042-022-13252-w
J. Wei, H. Huang, Y. Wang, R. He, and Z. Sun, "Towards More Discriminative and Robust Iris Recognition by Learning Uncertain Factors," IEEE Transactions on Information Forensics and Security, vol. 17, pp. 865–879, Feb. 2022, https://dx.doi.org/10.1109/tifs.2022.3154240
A. Iula, and M. Micucci, “Multimodal Biometric Recognition Based on 3D Ultrasound Palmprint-Hand Geometry Fusion,” IEEE Access, vol. 10, pp. 7914–7925, Jan. 2022, https://dx.doi.org/10.1109/access.2022.3143433
M. Mohualdeen, and M. Baker, “Gait Recognition Based on Silhouettes Sequences and Neural Networks for Human Identification,” Indonesian Journal of Electrical Engineering and Informatics (IJEEI), vol. 6, issue 1, pp. 110-117, March 2018, https://doi.org/10.52549/ijeei.v6i1.303
P. Kasprowski, Z. Borowska and K. Harezlak, “Biometric Identification Based on Keystroke Dynamics,” Sensors, vol. 22, issue 9, pp.3158, April 2022, https://dx.doi.org/10.3390/s22093158
M. T. S. Al-Kaltakchi, W. L Woo, S. Dlay and J. A. Chambers, “Evaluation of a speaker identification system with and without fusion using three databases in the presence of noise and handset effects,” EURASIP Journal on Advances in Signal Processing, vol. 2017, issue 1, pp. 1-17, Dec. 2017, https://dx.doi.org/10.1186/s13634-017-0515-7
M. T. S. Al-Kaltakchi, R. R. O. Al-Nima, M. A. M. Abdullah and H. N. Abdullah, “Thorough evaluation of TIMIT database speaker identification performance under noise with and without the G.712 type handset,” International Journal of Speech Technology, vol. 22, issue 3, pp. 851–863, Sep. 2019, https://dx.doi.org/10.1007/s10772-019-09630-9
M. T. S. Al-Kaltakchi, R. R. O. Al-Nima, M. Alfathe, and M. A. M. Abdullah, "Speaker Verification Using Cosine Distance Scoring with i-vector Approach,” in International Conference on Computer Science and Software Engineering (CSASE), 2020, https://dx.doi.org/10.1109/csase48920.2020.9142088
M. T. S. Al-Kaltakchi, M. A. M. Abdullah, W. L Woo, and S. S. Dlay, “Combined i-Vector and Extreme Learning Machine Approach for Robust Speaker Identification and Evaluation with SITW NIST 2008, TIMIT Databases,” Circuits Systems, and Signal Processing, vol. 40, issue 10, pp. 4903–4923, March 2021, https://dx.doi.org/10.1007/s00034-021-01697-7
S. Gornale, S. Kumar, R. Siddalingappa, and P. S. Hiremath, “Survey on Handwritten Signature Biometric Data Analysis for Assessment of Neurological Disorder using Machine Learning Techniques,” Transactions on Machine Learning and Artificial Intelligence, vol. 10, issue 2, pp. 27–60, April 2022, https://dx.doi.org/10.14738/tmlai.102.12210
T. Dhieb, H. Boubaker, S. Njah, M. Ben Ayed and A. M. Alimi, “A novel biometric system for signature verification based on score level fusion approach,” Multimedia Tools and Applications, vol. 81, issue 6, pp. 7817–7845, Jan. 2022, http://dx.doi.org/10.1007/s11042-022-12140-7
J. Galbally, R. Plamondon, J. Fierrez and J. Ortega-Garcia, “Synthetic on-line signature generation. Part I: Methodology and algorithms,” Pattern Recognition, vol. 45, issue 7, pp. 2610–2621, July 2012, https://dx.doi.org/10.1016/j.patcog.2011.12.011
J. Galbally, J. Fierrez, J. Ortega-Garcia, and R. Plamondon, “Synthetic on-line signature generation. Part II: Experimental validation,” Pattern Recognition, vol. 45, issue 7, pp. 2622– http://dx.doi.org/12632, July 2012, http://dx.doi.org/10.1016/j.patcog.2011.12.007
C OZ, F Ercal, and Z Demir, “Signature Recognition and Verification with ANN,” in 3rd International Conference on Electrical and Electronics Engineering, 2003, https://dx.doi.org/10.1007/11427445_31
M. H. Saffar, M. Fayyaz, M. Sabokrou, and M. Fathy, "Online signature verification using deep representation: a new descriptor,” ETRI Journal, Jun 2018, doi:10.48550/arXiv.1806.09986
H. Srinivasan, S. N. Srihari and M. J. Beal, “Machine Learning for Signature Verification,” in proc. Lecture Notes in Computer Science, 2006, pp. 761–775, https://dx.doi.org/10.1007/11949619_68
S. Marinai and S. Fujisawa, “Machine learning in document analysis and recognition,” Springer Science and Business Media, vol. 90, Dec. 2007, https://dx.doi.org/10.1007/978-3-540-76280-5
N. Çalik, O. C. Kurban, A. R. Yilmaz, T. Yildirim, and L. Durak Ata, “Large-scale offline signature recognition via deep neural networks and feature embedding,” Neurocomputing, vol. 359, pp. 1–14, Sep. 2019, https://dx.doi.org/10.1016/j.neucom.2019.03.027
J. Poddar, V. Parikh and, S. K. Bharti, “Offline Signature Recognition and Forgery Detection using Deep Learning,” Procedia Computer Science, vol. 170, pp. 610–617, Jan 2020, https://doi.org/10.1016/j.procs.2020.03.133
A. Hirunyawanakul, S. Bunrit, N. Kerdprasop, and K. Kerdprasop, “Deep learning technique for improving the recognition of handwritten signature,” Int. J. Inform. Electron. Eng, vol. 9. issue 4, Dec. 2019, https://dx.doi.org/10.18178/IJIEE.2019.9.4.709
P. Kiran, B. D. Parameshachari, J. Yashwanth, and K. N. Bharath, "Offline Signature Recognition Using Image Processing Techniques and Back Propagation Neuron Network System," SN Computer Science, vol. 2, issue 3, pp.196, April 2021, https://dx.doi.org/10.1007/s42979-021-00591-y
L. Junli, Y. Gengyun, and Z. Guanghui, "Evaluation of tobacco mixing uniformity based on chemical composition," in Proceedings of the 31st Chinese Control Conference, July 2012, pp. 7552-7555, https://dx.doi.org/10.4028/www.scientific.net/AMR.601.128
LV. Fauset, “Fundamentals of Neural Networks: architectures, algorithms and applications,” Inc.Division of Simon and Schuster One Lake Street Upper Saddle River, NJ, United States: Prentice-Hall, July 1994, pp. 461. https://dx.doi.org/10.5555/197023
MathWorks, Define Shallow Neural Network Architectures. Accessed: 2022. Available: https://www.mathworks.com/help/deeplearning/define-neural-network-architectures.html
A. Parsaie, “Predictive modeling the side weir discharge coefficient using neural network,” Modeling Earth Systems and Environment, vol. 2, issue 2, pp. 1-11, April 2016, https://dx.doi.org/10.1007/s40808-016-0123-9
A. A. Ismael, S. J. Suleiman, R. R. O. Al-Nima, and N. Al-Ansari, “Predicting the discharge coefficient of oblique cylindrical weir using neural network techniques,” Arabian Journal of Geosciences, vol. 14, issue 16, Aug. 2021, https://dx.doi.org/10.1007/s12517-021-07911-9
M. V. Shcherbakov, A. Brebels, N. L. Shcherbakova, A. P. Tyukov, T. A. Janovsky and V. A. E. Kamaev, “A survey of forecast error measures,” World applied sciences journal, vol. 24, issue 24, pp. 171-176, Jan. 2013, https://dx.doi.org/10.5829/idosi.wasj.2013.24.itmies.80032
M. R. Khalil, M. S. Majeed and R. R. Omar, “Personal identification with iris patterns,” AL-Rafidain Journal of Computer Sciences and Mathematics, vol 6, issue 1, pp. 13-26, 2009, available: https://www.mosuljournals.com/article_163762_d72ba50fed03de2dc0abc6e756155b1b.pdf
National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA). Available: https://nlpr.ia.ac.cn/en
Downloads
Key Dates
Received
Revised
Accepted
Published Online First
Published
Issue
Section
License
Copyright (c) 2024 Sadoon Alsumaydai, Wai Lok Woo (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
