Design and Development for Forgery Currency Detection using SIFT Features based SVM Classifier
M.Praneesh1, R.Nagarajan2, P.Kavitha3
1M.Praneesh, Assistant Professor, Dept of Computer Science, Sri Ramakrishna college of Arts and Science, Bharathiar University.
2R.Nagarajan, Assistant Professor, Dept of Computer Science, Sri Ramakrishna college of Arts and Science, Bharathiar University.
3P.Kavitha, Assistant Professor, Dept of Computer Science, Sri Ramakrishna college of Arts and Science, Bharathiar University.
Manuscript received on 19 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 29 June 2020 | PP: 135-137 | Volume-8 Issue-10S2 August 2019 | Retrieval Number: J102508810S19/2019©BEIESP | DOI: 10.35940/ijitee.J1025.08810S19
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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: There are many methods for identifying a fake currency notes which we have discussed and each one has its own significance. But, there is no software to detect fake currencies. The first process is to get the original and fake currency image from the data set .After getting the two images are pre-analysis the both original and fake image Convert the image into gray color. To extract the black strips in both currency images. After conversion the image segmentation are applied and the post-processing are applied. After that the feature extraction are classified undergoes SVM Classifier. SIFT Algorithm are used in the training set to count the black strips from both original and forgery image. Finally, there are two types of result will be executed under the histogram feature analysis and probability map and next one is to counting the black strips from both original and fake images.
Keywords: SVM Classifier, Fake Currency, SIFT Features, Pre Processing
Scope of the Article: Recent Trends & Developments in Computer Networks