Yemeni Mobile Counterfeit Detection System Using Support Vector Machines, Fuzzy Logic and Image Processing Techniques

AL-Gawda, Mohammed; Zou, Beiji; Mohammed, Nurudeen

doi:10.1166/jctn.2016.4945

Cited by 5 publications

(1 citation statement)

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“…In order to solve this problem and to present alternative solutions, in the state-of-theart, there are proposals based on classical computer vision techniques. For example, from histogram equalization [3], nearest neighbor interpolation [4], genetic algorithms [5] and fuzzy systems [6]. However, the main problem of this type of methods is its low capacity of generalization for new examples as well as its low accuracy.…”

Section: Introductionmentioning

confidence: 99%

Fake Banknote Recognition Using Deep Learning

2021

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Recently, some state-of-the-art works have used deep learning-based architectures, specifically convolutional neural networks (CNNs), for banknote recognition and counterfeit detection with promising results. However, it is not clear which design strategy is more appropriate (custom or by transfer learning) in terms of classifier performance and inference times for massive data applications. This paper presents a comparison of the two design strategies in various types of architecture. For the transfer learning (TL) strategy, the most appropriate freezing points in CNN architectures (sequential, residual and Inception) are identified. In addition, a custom model based on an AlexNet-type sequential CNN is proposed. Both the TL and the custom models were trained and compared using a Colombian banknote dataset. According to the results, ResNet18 achieved the best accuracy, with 100%. On the other hand, the network with the shortest inference times was the proposed custom network, since its performance is up to 6.48-times faster in CPU and 16.29-times faster in GPU than the inference time with the models by transfer learning.

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Section: Introductionmentioning

confidence: 99%