Real Time Fake Currency Note Detection using Deep Learning
M. Laavanya1, V. Vijayaraghavan2
1M. Laavanya, Department of Electronics and Communication Engineering, Vignan’s Foundation for Science Technology and Research, Vadlamudi, Guntur (Andhra Pradesh), India.
2V. Vijayaraghavan, Department of Electronics and Communication Engineering, Vignan’s Foundation for Science Technology and Research, Vadlamudi, Guntur (Andhra Pradesh), India.
Manuscript received on 23 November 2019 | Revised Manuscript received on 17 December 2019 | Manuscript Published on 30 December 2019 | PP: 95-98 | Volume-9 Issue-1S5 December 2019 | Retrieval Number: A10071291S52019/19©BEIESP | DOI: 10.35940/ijeat.A1007.1291S52019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Great technological advancement in printing and scanning industry made counterfeiting problem to grow more vigorously. As a result, counterfeit currency affects the economy and reduces the value of original money. Thus it is most needed to detect the fake currency. Most of the former methods are based on hardware and image processing techniques. Finding counterfeit currencies with these methods is less efficient and time consuming. To overcome the above problem, we have proposed the detection of counterfeit currency using a deep convolution neural network. Our work identifies the fake currency by examining the currency images. The transfer learned convolutional neural network is trained with two thousand, five hundred, two hundred and fifty Indian currency note data sets to learn the feature map of the currencies. Once the feature map is learnt the network is ready for identifying the fake currency in real time. The proposed approach efficiently identifies the forgery currencies of 2000, 500, 200, and 50 with less time consumption.
Keywords: Convolutional Neural Network, Currency Detection, Deep Learning, Feature Extraction, Image Processing.
Scope of the Article: Deep Learning