| Thakur & Kaur (2014) |
Review of fake currency detection techniques |
Survey paper |
Not applicable |
2014 |
| Chakraborty et al. (2013) |
Recent developments in paper currency recognition system |
Survey paper |
Not applicable |
2013 |
| Prasanthi & Setty (2015) |
Indian paper currency authentication system |
Image processing |
Performance is less than machine learning based systems |
2015 |
| Kang & Lee (2016) |
Fake banknote detection |
Multispectral imaging sensors |
Feature extraction and classification require high computation |
2016 |
| Mirza & Nanda (2012a) |
Currency verification |
Image processing: edge detection and image segmentation |
Only for Indian notes |
2012 |
| Snehlata & Saxena (2017) |
Fake currency identification |
UML activity model using class descriptors |
Only for Rs 2000 note of Indian currency |
2017 |
| Singh, Ozarde & Abhiram (2018) |
Detecting forged Indian currency |
Image processing, k-means clustering and SVM as a classifier |
Limited to Rs 500 note of Indian currency |
2018 |
| Abburu et al. (2017) |
Automated currency recognition for identifying country of origin and denomination |
Image processing |
Cannot detect counterfeit or forgery |
2017 |
| Ross et al. (2016) |
Database for detecting counterfeit items |
Digital fingerprint records using images of security features |
Performance is less than machine learning based systems |
2016 |
| Kayani (2017) |
Bank note processing system |
Florescence and phosphorescence detection |
Many security features are not detectable using florescence and phosphorescence detection |
2017 |
| Micali & Devadas (2017) |
Counterfeit prevention |
Physically unclonable value for unique identification for each currency note |
Needs Internet connection for sending images to centralized server |
2017 |
| Phillips (2018) |
Miniature counterfeit detector |
Back light illuminators are used for visual inspection of the 93 watermarks, florescent and anti-counterfeiting features |
Many security features are not detectable using florescence and phosphorescence detection |
2018 |
| Alicherry (2017) |
Verifying the authenticity of a currency note and tracking duplicate notes |
Digital signature based on the serial number of the currency note |
Needs Internet connection for sending images to centralized server |
2017 |
| Berenguel et al. (2016) |
Identify genuine bank notes |
Differentiate the texture between the original and photocopied notes using OFT |
Accuracy is less than machine learning based systems |
2016 |
| Choi et al. (2010) |
Counterfeit detection |
Characterization of safety feature on banknote with full-field optical coherence tomography |
Accuracy is less than machine learning based systems |
2010 |
| Hassanpour & Farahabadi (2009) |
Paper currency recognition |
Machine Learning: Hidden Markov Models |
Accuracy is less than the proposed system |
2009 |
| Mohamad et al. (2014) |
Banknote authentication |
Srtificial neural network |
Accuracy is less than the proposed system |
2014 |
