Document Type : Original/Review Paper

Authors

• Mohammad M. AlyanNezhadi ¹
• Hesamoddin Pourrostami ²
• Mousa Nazari ³
• Farzan Afshari ⁴

¹ University of Science and Technology of Mazandaran

² International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology

³ Department of Computer Science, University of Science and Technology of Mazandaran, Behshahr, Mazandaran, Iran

⁴ Department Of Physics, Semnan University, Semnan, Iran

Abstract

In Iran’s financial market, the authentication of gold coins is majorly required for transparency, reducing fraud, and proper valuation. Differentiating between bank-issued and non-bank-issued coins pose a challenge as their appearance is almost the same. This paper suggests a classification method that is based on deep learning and has three main components: extracting area of interest, aligning images through a CNN regressor, and classifying coins through a CNN classifier. The method is tested on a set of 130 coins images (71 coins from banks and 59 coins from non-banks) and is benchmarked against baseline models employing feature extraction and SVMs. The proposed method outperforms the baseline with 99% accuracy. The results prove that the model works effectively in authenticating the coins, which enables safe transactions in the gold market.

Keywords

• Convolutional Neural Network
• Image Processing
• Coin Detecting
• Forgery Detection

Main Subjects

• H.5. Image Processing and Computer Vision