Counterfeit detection of ringgit banknotes using image processing techniques

Many of us are lucky to be able to see the world and go about our daily activities. Around the globe and in every part of the world, there are Visually impaired persons (VIPs), and they comprise a significant portion of the population. The visually impaired person may often face difficulty in readin...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Salem Al-Hila, Turki Khaled
التنسيق:	أطروحة
منشور في:	2023
الموضوعات:	TA1501-1820 Applied optics Photonics
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id	my-mmu-ep.12873
record_format	uketd_dc
spelling	my-mmu-ep.128732024-08-28T09:13:59Z Counterfeit detection of ringgit banknotes using image processing techniques 2023-02 Salem Al-Hila, Turki Khaled TA1501-1820 Applied optics. Photonics Many of us are lucky to be able to see the world and go about our daily activities. Around the globe and in every part of the world, there are Visually impaired persons (VIPs), and they comprise a significant portion of the population. The visually impaired person may often face difficulty in reading banknotes. Malaysian researchers have proposed Ringgit banknote recognition systems for the visually impaired, but they only recognise banknote value, lacking counterfeit detection. Further detecting fake banknote is still a problem waiting to be solved by the banknote recognition system developers to improve accessibility and recognition for visually impaired users. This research discusses Malaysian banknote counterfeit detection algorithms that make use of image processing techniques and fuzzy logic algorithms. Image processing is a fast-growing technology that has impacted many businesses and industries. Furthermore, this research proposed two novel fuzzy logic algorithms for detecting counterfeit Malaysian banknotes: Fuzzy Logic based Weighted Averaging algorithm (FLWA) and Fuzzy Logic based Weighted Specific algorithm (FLWS). The FLWA and FLWS Malaysian Banknote Counterfeit Detection algorithms were intercompared with other parallel methods (MobileNet model using RMSprop Loss Function (learning_rate=0.0001) and VGG16 model using 2D Convolution Layer (32 neural) at TensorFlow's Keras API). In terms of accuracy, processing speed, and complexity, FLWA and FLWS outperform the TWO parallel methods shown in the experimental results. In addition, the FLWA and FLWS Malaysian Banknote Counterfeit Detection algorithms are intra-compared. However, FLWS’s accuracy is much higher than FLWA's in detecting Malaysian Counterfeit Banknotes. The number of security features is restricted to nine security elements. Banknotes were extracted and encoded using image processing techniques. Several security features, such as the magnifier and feeling mechanism, require a high-quality tool or an extension tool to be extracted. The Ringgit Counterfeit detection system was tested at the Malaysian Association for the Blind (MAB) and received positive feedback from participants. 2023-02 Thesis https://shdl.mmu.edu.my/12873/ http://erep.mmu.edu.my/ masters Multimedia University Faculty of Engineering and Technology (FET) EREP ID: 12297
institution	Multimedia University
collection	MMU Institutional Repository
topic	TA1501-1820 Applied optics Photonics
spellingShingle	TA1501-1820 Applied optics Photonics Salem Al-Hila, Turki Khaled Counterfeit detection of ringgit banknotes using image processing techniques
description	Many of us are lucky to be able to see the world and go about our daily activities. Around the globe and in every part of the world, there are Visually impaired persons (VIPs), and they comprise a significant portion of the population. The visually impaired person may often face difficulty in reading banknotes. Malaysian researchers have proposed Ringgit banknote recognition systems for the visually impaired, but they only recognise banknote value, lacking counterfeit detection. Further detecting fake banknote is still a problem waiting to be solved by the banknote recognition system developers to improve accessibility and recognition for visually impaired users. This research discusses Malaysian banknote counterfeit detection algorithms that make use of image processing techniques and fuzzy logic algorithms. Image processing is a fast-growing technology that has impacted many businesses and industries. Furthermore, this research proposed two novel fuzzy logic algorithms for detecting counterfeit Malaysian banknotes: Fuzzy Logic based Weighted Averaging algorithm (FLWA) and Fuzzy Logic based Weighted Specific algorithm (FLWS). The FLWA and FLWS Malaysian Banknote Counterfeit Detection algorithms were intercompared with other parallel methods (MobileNet model using RMSprop Loss Function (learning_rate=0.0001) and VGG16 model using 2D Convolution Layer (32 neural) at TensorFlow's Keras API). In terms of accuracy, processing speed, and complexity, FLWA and FLWS outperform the TWO parallel methods shown in the experimental results. In addition, the FLWA and FLWS Malaysian Banknote Counterfeit Detection algorithms are intra-compared. However, FLWS’s accuracy is much higher than FLWA's in detecting Malaysian Counterfeit Banknotes. The number of security features is restricted to nine security elements. Banknotes were extracted and encoded using image processing techniques. Several security features, such as the magnifier and feeling mechanism, require a high-quality tool or an extension tool to be extracted. The Ringgit Counterfeit detection system was tested at the Malaysian Association for the Blind (MAB) and received positive feedback from participants.
format	Thesis
qualification_level	Master's degree
author	Salem Al-Hila, Turki Khaled
author_facet	Salem Al-Hila, Turki Khaled
author_sort	Salem Al-Hila, Turki Khaled
title	Counterfeit detection of ringgit banknotes using image processing techniques
title_short	Counterfeit detection of ringgit banknotes using image processing techniques
title_full	Counterfeit detection of ringgit banknotes using image processing techniques
title_fullStr	Counterfeit detection of ringgit banknotes using image processing techniques
title_full_unstemmed	Counterfeit detection of ringgit banknotes using image processing techniques
title_sort	counterfeit detection of ringgit banknotes using image processing techniques
granting_institution	Multimedia University
granting_department	Faculty of Engineering and Technology (FET)
publishDate	2023
_version_	1811768013845692416

Counterfeit detection of ringgit banknotes using image processing techniques

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