Main Article Content
This paper proposed a novel fuzzy logic weighted averaging (FLWA) algorithm in image processing techniques to detect counterfeit Malaysian banknotes. Image acquisition techniques on banknote position detection and re-adjustment, image pre-processing techniques, feature extraction methods on Malaysian banknotes’ watermarks are also covered in the paper. The FLWA Algorithm has the advantage of a much simpler model since it is a human guidance learning algorithm that does not require enrolment process to get the specific weights for each security feature. Each security feature is treated with equal weight. The experimental results also shown that FLWA model also outperform the MobileNet model and VGG16 model in Malaysian banknotes’ counterfeit detection. It has a distinct advantage over earlier or current banknote counterfeit detection techniques in that it adopted the known watermarks features, with known machine learning techniques to identify real Malaysian banknotes and detect those counterfeit Malaysian banknotes.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Z. Ahmed, S. Yasmin, M. Nahidul Islam and R. U. Ahmed, "Image Processing Based Feature Extraction of Bangladeshi Banknotes," in The 8th Int. Conf. Software, Knowledge, Inform. Manage. and Appl., Dhaka, pp. 1-8, 2014.
A. Tushar, B. Gajanan, W. Pratik and P. Chitra, “Fake Currency Detection Using Image Processing,” in IOP Conf. Series: Mater. Sci. and Eng., 263, 052047, 2017.
S. Mrutunjay, O. Preetam and A. Konidena. “Image Processing Based Detection of Counterfeit Indian Bank Notes,” in 9th Int. Conf. Comput., Comm. and Network. Technol., pp. 1-5, 2018.
S. M. Sabat and N. A. Twana, “Image-Based Processing of Paper Currency Recognition and Fake Identification: A Review,” Technium, vol. 3, no. 7, pp. 46-63, 2021.
J. M. R. Apoloni, S. D. G. Escueta and J. T. Sese, "Philippine Currency Counterfeit Detector using Image Processing," in IEEE 18th Int. Colloq. Signal Process. & Appl., Selangor, pp. 436-441, 2022.
A. Z. Lotfi and A. A. Rafik, Fuzzy Logic Theory and Applications: Part I And Part II, World Scientific, 2018.
C. Jenny, C. Francisco, S. K. Arjab and C. Tianhua, Fuzzy Logic: Recent Applications and Developments, Springer Nature, 2021.
D. T. Aseffa, H. Kalla and S. Mishra, “Ethiopian Banknote Recognition Using Convolutional Neural Network and Its Prototype Development Using Embedded Platform”, J. Sensors, vol. 2022, 4505089, pp. 1-18, 2022.
S. Nanda, M. Abbas, N. Momaya and K. A. Mahesh, “Indian Currency Detection for Blind People with VGG16,” Int. J. Innov. Res. in Technol., vol. 8, no. 2, pp. 541-544, 2021.
M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine Learning, The MIT Press, 2012.
S. Patel, R. Nargunde, C. Shah and S. Dholay, “Counterfeit Currency Detection using Deep Learning,” in 12th Int. Conf. Comput. Comm. and Network. Techn., Kharagpur, pp. 1-5, 2021.
D. Saire and S. Tabbone, “Documents Counterfeit Detection Through A Deep Learning Approach,” in Proc. Int. Conf. Pattern Recogn., pp. 3915–3922, 2020.
K. N. N. Hlaing and A. K. Gopalakrishnan, “Myanmar Paper Currency Recognition Using GLCM and k-NN,” in 2nd Asian Conf. Def. Technol., 67–72, 2016.
C. Rahmad, E. Rohadi and R. A. Lusiana,“Authenticity of Money Using the Method KNN (K-Nearest Neighbor) and CNN (Convolutional Neural Network),” in IOP Conf. Series: Mater. Sci. and Eng., vol. 1073, 012029, 2021.
I. A. Doush and A. B. Sahar, “Currency Recognition Using A Smartphone: Comparison Between Color SIFT and Gray Scale SIFT Algorithms,” J. King Saud Univ. - Comput. Inf. Sci., vol. 29(4), pp. 484–492, 2017.
G. Anahita, A. Jamilu and A. B. Azuraliza, “Comparative Analysis of Algorithms in Supervised Classification: A Case study of Bank Notes Dataset,” Int. J. Comput. Trends and Technol., vol. 17, no. 1, pp. 39-43, 2014.
N.A. Jasmin Sufri, N.A. Rahmad, M.A. As’ari, N.A. Zakaria, M.N. Jamaludin, L.H. Ismail and N.H. Mahmood, "Image Based Ringgit Banknote Recognition for Visually Impaired," J. Telecom. Electron. and Comput. Eng., vol. 9, no. 3–9, pp. 103-111, 2017.
N. A. J. Sufri, N. A. Rahmad, N. F. Ghazali, N. Shahar and M. A. As’ari, "Vision Based System for Banknote Recognition Using Different Machine Learning and Deep Learning Approach," in IEEE 10th Contr. and Sys. Graduate Res. Colloq., Shah Alam, pp. 5-8, 2019.
S. Gopane and R. Kotecha, “Indian Counterfeit Banknote Detection Using Support Vector Machine,” SSRN Electron. J., 3568724, 2020.
C. Yeh, W. Su and S. Lee, “Employing Multiple-kernel Support Vector Machines for Counterfeit Banknote Recognition,” Appl. Soft Comput., vol. 11, no. 1, pp. 1439-1447, 2011
B. Huaytalla, D. Humari and G. Kemper, "An algorithm for Peruvian counterfeit Banknote Detection Based on Digital Image Processing and SVM," Advance. Sci., Technol. and Eng. Sys. J., vol. 6, no. 1, pp. 1171-1178, 2021.
W. K. Wong, C. J. Tan, T. S. Min and E. K. Wong, “Fuzzy Logic Based Perceptual Image Hashing Algorithm in Malaysian Banknotes Detection System for the Visually Impaired,” Artif. Intellig. Advanc., vol. 3, no. 1, pp. 52–64, 2021.
Bank Negara Malaysia, “Current Banknote Series,” Central Bank of Malaysia. https://www.bnm.gov.my/current-banknote-series (accessed on Aug. 17, 2022).
S. Bhutada, N. Yashwanth, P. Dheeraj and K. Shekar, “Opening and Closing in Morphological Image Processing,” World J. Advanc. Res. and Rev., vol. 14, no. 3, pp. 687–695, 2022.
W. S. Alazawee, I. Abdel-Qader and J. Abdel-Qader, “Using Morphological Operations - Erosion Based Algorithm for Edge Detection,” in IEEE Int. Conf. Electron. Inform. Technol., vol. 2015-June, pp. 521–525, 2015.
B. Y. Nesrine, B. B. Saoud and B. G. Henda, “Integrating Fuzzy Case-based Reasoning and Particle Swarm Optimization to Support Decision Making.” Int. J. Comput. Sci., no. 9(3), pp. 117-124, 2012.
S. Ali, Introductory Chapter: Which Membership Function is Appropriate in Fuzzy System?, 79552, 2018.
W. K. Ling (2007). Chapter 2 – REVIEWS, Nonlinear Digital Filters, Academic Press, pp. 8-31, 2007.