Ringgit Value and Counterfeit Detection Using Image Processing Techniques


Salem, Turki Khaled and Wong, Wai Kit and Min, Thu Soe @ M Sait (2022) Ringgit Value and Counterfeit Detection Using Image Processing Techniques. In: 2nd FET PG Engineering Colloquium Proceedings 2022, 1-15 December 2022, Multimedia University, Malaysia. (Unpublished)

[img] Text
09_Turki Khaled__PPM Extended Abstract_Second Half 2022.pdf
Restricted to Registered users only

Download (98kB)


Background - Many of us are lucky to be able to see the world and get around on our own. Around the globe and in every part of the world they are Visually impaired persons (VIPs), and they are comprising a significant portion of the population. The visually impaired person facing difficulty in reading banknotes. Purpose – Many Ringgit banknote recognition systems had been proposed by Malaysian researchers to aid the visually impaired person in recognizing and classifying Ringgit banknotes. However, these electronic banknote recognizers can only work for recognizing Malaysian Banknote Ringgit value, with no counterfeit detection being implemented. Design/methodology/approach – 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). Findings – The FLWA and FLWS Malaysian Banknote Counterfeit Detection algorithms were intercompared with THREE parallel methods (VGG16 model using 2D Convolution Layer (32 neural) at TensorFlow's Keras API, MobileNet model using RMSprop Loss Function (learning_rate=0.0001) at TensorFlow's Keras API and Fuzzy Logic Based Perceptual Image Hashing Algorithm). In terms of accuracy, processing speed, and complexity, FLWA and FLWS outperform the three parallel methods shown in the experimental results. In addition, the FLWA and FLWS Malaysian Banknote Counterfeit Detection algorithms are intra-compared. Also, the experimental results show that in terms of accuracy, processing speed, and complexity, FLWA outperforms FLWS in Malaysian Banknote Counterfeit Detection.However, FLWS’s accuracy is much higher than FLWA's in detecting Malaysian Banknote Counterfeits. Research limitations– The number of security features are restricted to nine security elements banknotes were extracted and encoded using image processing techniques. Because several security features, such as the magnifier and feeling mechanism, require a high-quality tool or an extension tool to be extracted. Originality/value – The Ringgit Counterfeit detection system was tested at Malaysian Association for the Blind (MAB) and received positive feedback from participants.

Item Type: Conference or Workshop Item (Other)
Uncontrolled Keywords: Image Processing, Banknote Reader, Banknote Counterfeit, Ringgit Detector
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 16 Feb 2023 04:33
Last Modified: 16 Feb 2023 04:33
URII: http://shdl.mmu.edu.my/id/eprint/10729


Downloads per month over past year

View ItemEdit (login required)