Abstract
In recent years, security of currency has gain importance in the field of research. With the advent of digital technology, color printer, and color scanner are the cheapest way for counterfeiter to produce fake currency. Feature extraction is the most important technique in paper currency recognition. According to reviewer, texture feature plays an important role for paper currency detection. Texture feature is generally a statistical-based approach and in the present work, a new model is proposed for paper currency detection. The presented model is computing the texture properties like Gray Level Co-occurrence Matrix (GLCM) of Rs. 500 for real and fake currency. The Principle Component Analysis (PCA) is used for reduction of higher dimension of images. The proposed work provides better results with the collaboration of PCA and GLCM. The texture properties have been used and GLCM measured the variation in intensity at pixel of interest of the currency. The computed results have been presented in the form of table and graphs.
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Snehlata, Saxena, V. (2021). Texture Feature Technique for Security of Indian Currency. In: Singh, P.K., Singh, Y., Kolekar, M.H., Kar, A.K., Chhabra, J.K., Sen, A. (eds) Recent Innovations in Computing. ICRIC 2020. Lecture Notes in Electrical Engineering, vol 701. Springer, Singapore. https://doi.org/10.1007/978-981-15-8297-4_55
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DOI: https://doi.org/10.1007/978-981-15-8297-4_55
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