Advertisement

Overview of currency recognition using deep learning

  • Qian Zhang
  • Wei Qi YanEmail author
  • Mohan Kankanhalli
Original Article
First Online:
  • 42 Downloads

Abstract

The human visual system could be used for recognizing and authenticating currency notes. However, the observation powers of our eyes are limited, and it is often difficult for us to recognize genuine currency without any technological assistance. Deep learning techniques have shown to be effective and superior for many applications. They have been particularly successful in surpassing human visual recognition capabilities when big data is employed. Therefore deep learning has been employed to improve the accuracy of currency recognition. After studying the existing currency identification literature, we present a comprehensive overview of currency recognition. We also discuss the critical problem of augmenting the limited amount of data available. Our contributions include summarizing the deep learning approaches such as CNN, SSD, MLP, etc. We also present our attempt to apply deep learning to currency recognition, with the aim of improving the existing recognition accuracy. The paper also describes in brief the potential future directions for research.

Keywords

Currency recognition Deep learning Future directions 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Abdel-Hamid O, Mohamed AR, Jiang H, Penn G (2012) Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. In: IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, Kyoto, Japan, pp 4277–4280Google Scholar
  2. 2.
    Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916CrossRefGoogle Scholar
  3. 3.
    Basu AP, Ebrahimi N (1991) Bayesian approach to life testing and reliability estimation using asymmetric loss function. J Stat Plan Inference 29(1–2):21–31MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Bharkad AASS (2013) Survey of currency recognition system using image processing. Int J Comput Eng Res 3(7):126–128Google Scholar
  6. 6.
    Bobrowski L (1978) Learning processes in multilayer threshold nets. Biol Cybern 31(1):1–6zbMATHCrossRefGoogle Scholar
  7. 7.
    Chambers J (2013) Digital currency forensics. Masters dissertation, Auckland University of TechnologyGoogle Scholar
  8. 8.
    Ciresan DC, Meier U, Masci J, Maria Gambardella L, Schmidhuber J (2011) Flexible, high performance convolutional neural networks for image classification. IJCAI 22(1):1237Google Scholar
  9. 9.
    Dai J, Li Y, He K, Sun J (2016) R-fcn: object detection via region-based fully convolutional networks. In: Advances in neural information processing systems, pp 379–387Google Scholar
  10. 10.
    Debnath KK, Ahmed SU, Shahjahan M, Murase K (2010) A paper currency recognition system using negatively correlated neural network ensemble. J Multimed 5(6):560CrossRefGoogle Scholar
  11. 11.
    Deng L, Li J, Huang JT, Yao K, Yu D, Seide F, Gong Y (2013) Recent advances in deep learning for speech research at Microsoft. In: ICASSP, vol 26, p 64Google Scholar
  12. 12.
    Deng L, Yu D (2014) Deep learning: methods and applications. Found Trends Signal Process 7(34):197–387MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Dollr P, Zitnick CL (2013) Structured forests for fast edge detection. In: Proceedings of the IEEE international conference on computer vision, pp 1841–1848Google Scholar
  14. 14.
    Epshtein B, Ullman S (2005) Feature hierarchies for object classification. In: ICCV 2005. IEEE, Kyoto, Japan, pp 220–227Google Scholar
  15. 15.
    Fan Z, Wu JW, Micco FA, Chen MC, Phong KA (2001) U.S. Patent No. 6,181,813. U.S. Patent and Trademark Office, Washington, DCGoogle Scholar
  16. 16.
    Feng LJ, Bo LS, Long TX (2003) An algorithm of real-time paper currency recognition. J Comput Res Dev 7:1057–1061Google Scholar
  17. 17.
    Frosini A, Gori M, Priami P (1996) A neural network-based model for paper currency recognition and verification. IEEE Trans Neural Netw 7(6):1482–1490CrossRefGoogle Scholar
  18. 18.
    Garg R, BG, VK, Carneiro G, Reid I (2016) Unsupervised CNN for single view depth estimation: Geometry to the rescue. In: European conference on computer vision. Springer, pp 740–756Google Scholar
  19. 19.
    Gehler P, Nowozin S (2009) On feature combination for multiclass object classification. In: IEEE international conference on computer vision, pp 221–228Google Scholar
  20. 20.
    Gunaratna DAKS, Kodikara ND, Premaratne HL (2008) ANN based currency recognition system using compressed gray scale and application for Sri Lankan currency notes-SLCRec. Proc World Acad Sci Eng Technol 35:235–240Google Scholar
  21. 21.
    Guo H, Gelfand SB (1992) Classification trees with neural network feature extraction. IEEE Trans Neural Netw 3(6):923–933CrossRefGoogle Scholar
  22. 22.
    Huang K, Yan H (1997) Off-line signature verification based on geometric feature extraction and neural network classification. Pattern Recognit 30(1):9–17CrossRefGoogle Scholar
  23. 23.
    Hasanuzzaman FM, Yang X, Tian Y (2011) Robust and effective component-based banknote recognition by SURF features. In: Wireless and optical communications conference (WOCC). IEEE, Kyoto, Japan, pp 1–6Google Scholar
  24. 24.
    Hassanpour H, Farahabadi PM (2009) Using hidden Markov models for paper currency recognition. Expert Syst Appl 36(6):10105–10111CrossRefGoogle Scholar
  25. 25.
    Hoo-Chang S, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285CrossRefGoogle Scholar
  26. 26.
    Huang W, Qiao Y, Tang X (2014) Robust scene text detection with convolution neural network induced mser trees. In: European conference on computer vision. Springer, Cham, pp 497–511Google Scholar
  27. 27.
    Janocha K, Czarnecki WM (2017) On loss functions for deep neural networks in classification. arXiv:1702.05659
  28. 28.
    Javed O, Shah M (2002) Tracking and object classification for automated surveillance. In: European conference on computer vision. Springer, Berlin, pp 343–357Google Scholar
  29. 29.
    Kim Y (2014) Convolutional neural networks for sentence classification. arXiv:1408.5882
  30. 30.
    Ko YH, Kim KJ, Jun CH (2005) A new loss function-based method for multiresponse optimization. J Qual Technol 37(1):50–59CrossRefGoogle Scholar
  31. 31.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105Google Scholar
  32. 32.
    Lauer F, Suen CY, Bloch G (2007) A trainable feature extractor for handwritten digit recognition. Pattern Recognit 40(6):1816–1824zbMATHCrossRefGoogle Scholar
  33. 33.
    Le QV, Ngiam J, Coates A, Lahiri A, Prochnow B, Ng AY (2011) On optimization methods for deep learning. In: Proceedings of the 28th international conference on international conference on machine learning. Omnipress, Kyoto, Japan, pp 265–272Google Scholar
  34. 34.
    Liao M, Shi B, Bai X, Wang X, Liu W (2017) TextBoxes: a fast text detector with a single deep neural network. In: AAAI, pp 4161–4167Google Scholar
  35. 35.
    Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: single shot multibox detector. In: European conference on computer vision. Springer, Cham, pp 21–37Google Scholar
  36. 36.
    Li F, Yang Y (2003) A loss function analysis for classification methods in text categorization. In: Proceedings of the 20th international conference on machine learning (ICML-03), pp 472–479Google Scholar
  37. 37.
    Masci J, Meier U, Cirean D, Schmidhuber J (2011) Stacked convolutional auto-encoders for hierarchical feature extraction. In: International conference on artificial neural networks. Springer, Berlin, pp 52–59Google Scholar
  38. 38.
    Merrinboer B, Bahdanau D, Dumoulin V, Serdyuk D, Warde-Farley D, Chorowski J, Bengio Y (2015) Blocks and fuel: frameworks for deep learning. arXiv:1506.00619
  39. 39.
    Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E (2015) Deep learning applications and challenges in big data analytics. J Big Data 2(1):1CrossRefGoogle Scholar
  40. 40.
    Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng AY (2011) Multimodal deep learning. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 689–696Google Scholar
  41. 41.
    Ren Y (2017) Banknote recognition in real time using ANN. Masters dissertation, Auckland University of TechnologyGoogle Scholar
  42. 42.
    Reponen E, Huuskonen P, Mihalic K (2008) Primary and secondary context in mobile video communication. Pers Ubiquitous Comput 12(4):281–288CrossRefGoogle Scholar
  43. 43.
    Rriedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 38(2):337–374MathSciNetzbMATHCrossRefGoogle Scholar
  44. 44.
    Sahiner B, Chan HP, Petrick N, Wei D, Helvie MA, Adler DD, Goodsitt MM (1996) Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Imaging 15(5):598–610CrossRefGoogle Scholar
  45. 45.
    Sainath TN, Mohamed AR, Kingsbury B, Ramabhadran B (2013) Deep convolutional neural networks for LVCSR. In: IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 8614–8618Google Scholar
  46. 46.
    Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117CrossRefGoogle Scholar
  47. 47.
    Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2013) Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv:1312.6229
  48. 48.
    Shen W, Wang X, Wang Y, Bai X, Zhang Z (2015) Deepcontour: a deep convolutional feature learned by positive-sharing loss for contour detection. In: IEEE conference on computer vision and pattern recognition, pp 3982–3991Google Scholar
  49. 49.
    Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
  50. 50.
    Song Z, Chen Q, Huang Z, Hua Y, Yan S (2011) Contextualizing object detection and classification. In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Kyoto, Japan, pp 1585–1592Google Scholar
  51. 51.
    Sun Y, Wang X, Tang X (2013) Deep convolutional network cascade for facial point detection. In: IEEE conference on computer vision and pattern recognition, pp 3476–3483Google Scholar
  52. 52.
    Sutskever I, Martens J, Dahl G, Hinton G (2013) On the importance of initialization and momentum in deep learning. In: International conference on machine learning, pp 1139–1147Google Scholar
  53. 53.
    Takeda F, Omatu S (1995) A neuro-paper currency recognition method using optimized masks by genetic algorithm. In: IEEE international conference on systems, man and cybernetics, vol 5, pp 4367–4371Google Scholar
  54. 54.
    Vedaldi A, Lenc K (2015) Matconvnet: convolutional neural networks for matlab. In: ACM international conference on multimedia. ACM, New York, NY, USA, pp 689–692Google Scholar
  55. 55.
    Waibel A (1989) Modular construction of time-delay neural networks for speech recognition. Neural Comput 1(1):39–46CrossRefGoogle Scholar
  56. 56.
    Wen Y, Zhang K, Li Z, Qiao Y (2016) A discriminative feature learning approach for deep face recognition. In: European conference on computer vision. Springer, Cham, pp 499–515Google Scholar
  57. 57.
    Witschorik CA (2000) U.S. Patent No. 6,131,718. U.S. Patent and Trademark Office, Washington, DCGoogle Scholar
  58. 58.
    Xu L, Ren JS, Liu C, Jia J (2014) Deep convolutional neural network for image deconvolution. In: Advances in neural information processing systems, pp 1790–1798Google Scholar
  59. 59.
    Yadav BP, Patil CS, Karhe RR, Patil PH (2014) An automatic recognition of fake Indian paper currency note using MATLAB. Int J Eng Sci Innov Technol 3:560–566Google Scholar
  60. 60.
    Yeh CY, Su WP, Lee SJ (2011) Employing multiple-kernel support vector machines for counterfeit banknote recognition. Appl Soft Comput 11(1):1439–1447CrossRefGoogle Scholar
  61. 61.
    Yu D, Deng L (2011) Deep learning and its applications to signal and information processing. IEEE Signal Process Mag 28(1):145–154CrossRefGoogle Scholar
  62. 62.
    Zanaty EA (2012) Support vector machines (SVMs) versus multilayer perception (MLP) in data classification. Egypt Inform J 13(3):177–183CrossRefGoogle Scholar
  63. 63.
    Zhang EH, Jiang B, Duan JH, Bian ZZ (2003) Research on paper currency recognition by neural networks. In: International conference on machine learning and cybernetics, vol 4, pp 2193–2197Google Scholar
  64. 64.
    Zhao R, Ouyang W, Li H, Wang X (2015) Saliency detection by multi-context deep learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1265–1274Google Scholar

Copyright information

© Institute for Development and Research in Banking Technology 2019

Authors and Affiliations

  1. 1.Auckland University of TechnologyAucklandNew Zealand
  2. 2.National University of SingaporeSingaporeSingapore

Personalised recommendations

Cite article

Buy options