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The research on banknote authenticity discrimination analysis algorithm based on wavelet transform features

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DOI: 10.23977/jaip.2024.070108 | Downloads: 14 | Views: 237


Zhao Sijian 1


1 School of Statistics and Mathematics, Yunnan University of Finance and Economics, Wuhua District, Kunming City, Yunnan Province, 650221, China

Corresponding Author

Zhao Sijian


In the authenticity identification of banknotes, features such as variance, skewness, kurtosis, and entropy of the images transformed by wavelet are used. This paper combines distance discriminant analysis, Fisher discriminant analysis, and Bayesian discriminant analysis for discrimination analysis. Variance can measure the texture complexity and grayscale level variation in the image, skewness is used to evaluate the symmetry and deviation from the normal distribution of the image, and kurtosis can measure the texture structure and grayscale level concentration. The entropy of the image reflects the complexity and uncertainty of the image. These features can be used to distinguish genuine banknotes from counterfeit ones.


Discriminant analysis, distance discriminant method, Fisher discriminant, Bayesian discriminant, authenticity identification


Zhao Sijian, The research on banknote authenticity discrimination analysis algorithm based on wavelet transform features. Journal of Artificial Intelligence Practice (2024) Vol. 7: 46-53. DOI:


[1] Zhou, C. (2023). Study on the Wear Law of the Technical Characteristics of RMB Anti-Counterfeiting—Based on the Detection and Analysis of the Fifth Set of RMB 100 Banknotes in 2015 Edition. Journal of Tsinghua University (Science and Technology), 01-167-07.
[2] Men Xiuping. Application of Wavelet Transform in Image Processing [J]. Journal of Information Engineering, Anhui University of Finance and Economics, 2024, 2(19-24).
[3] Fei, Y., & Chen, Y. (2014). Multivariate statistical analysis. Beijing: Renmin University of China Press.

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